DRAM is a critical component of modern computing systems. Recent works propose numerous techniques (that we call DRAM techniques) to enhance DRAM-based computing systems’ throughput, reliability, and computing capabilities (e.g., in-DRAM bulk data copy). Evaluating the system-wide benefits of DRAM techniques is challenging as they often require modifications across multiple layers of the computing stack. Prior works propose FPGA-based platforms for rapid end-to-end evaluation of DRAM techniques on real DRAM chips. Unfortunately, existing platforms fall short in two major aspects: (1) they require deep expertise in hardware description languages, limiting accessibility; and (2) they are not designed to accurately model modern computing systems.We introduce EasyDRAM, an FPGA-based framework for rapid and accurate end-to-end evaluation of DRAM techniques on real DRAM chips. EasyDRAM overcomes the main drawbacks of prior FPGA-based platforms with two key ideas. First, EasyDRAM removes the need for hardware description language expertise by enabling developers to implement DRAM techniques using a high-level language (C++). At runtime, EasyDRAM executes the high-level software-defined memory system design in a programmable memory controller. Second, EasyDRAM tackles a fundamental challenge in accurately modeling modern systems: real processors typically operate at significantly higher clock frequencies than DRAM, a disparity that is difficult to replicate on FPGA platforms. EasyDRAM addresses this challenge by decoupling the processor–DRAM interface and advancing the system state using a novel technique we call time scaling, which faithfully captures the timing behavior of the modeled system.We validate EasyDRAM’s evaluation accuracy by comparing the memory latency profile of a real CPU-based system and its modeled implementation using EasyDRAM. We demonstrate the ease of use of EasyDRAM by evaluating two DRAM techniques end-to-end in a real FPGA-based system: (1) in-DRAM bulk data copy (i.e., RowClone) and (2) reduced-latency DRAM access that exploits the latency variation across DRAM cells. Implementing these two techniques requires no hardware modifications and only 325 lines of C++ code over EasyDRAM’s extensible code base. We compare our results to prior FPGA-based platforms. EasyDRAM yields more accurate results (e.g., by ≈20× for execution time) than the state-of-the-art related platform. We believe and hope that EasyDRAM will enable innovative ideas in memory system design to rapidly come to fruition. To aid future research, we open-source our EasyDRAM implementation at https://github.com/CMU-SAFARI/EasyDRAM.

Read disturbance in modern DRAM is an important robustness (security, safety, and reliability) problem, where repeatedly accessing (hammering) a row of DRAM cells (DRAM row) induces bitflips in other physically nearby DRAM rows. Shrinking technology node size exacerbates DRAM read disturbance over generations. To help mitigate read disturbance, the latest DDR5 specifications (as of April 2024) introduced a new RowHammer mitigation framework, called Per Row Activation Counting (PRAC). PRAC 1) enables the DRAM chip to accurately track row activations by allocating an activation counter per row and 2) provides the DRAM chip with the necessary time window to perform RowHammer-preventive refreshes by introducing a new back-off signal. Unfortunately, no prior work rigorously studies PRAC’s security guarantees and overheads. In this paper, we 1) present the first rigorous security, performance, energy, and cost analyses of PRAC and 2) propose Chronus, a new mechanism that addresses PRAC’s two major weaknesses. Our analysis shows that PRAC’s system performance overhead on benign applications is non-negligible for modern DRAM chips and prohibitively large for future DRAM chips that are more vulnerable to read disturbance. We identify two weaknesses of PRAC that cause these overheads. First, PRAC increases critical DRAM access latency parameters due to the additional time required to increment activation counters. Second, PRAC performs a constant number of preventive refreshes at a time, making it vulnerable to an adversarial access pattern, known as the wave attack, and consequently requiring it to be configured for significantly smaller activation thresholds. To address PRAC’s two weaknesses, we propose a new on-DRAM-die RowHammer mitigation mechanism, Chronus. Chronus 1) updates row activation counters concurrently while serving accesses by separating counters from the data and 2) prevents the wave attack by dynamically controlling the number of preventive refreshes performed. Our performance analysis shows that Chronus’s system performance overhead is near-zero for modern DRAM chips and very low for future DRAM chips. Chronus outperforms three variants of PRAC and three other state-of-the-art read disturbance solutions. We discuss Chronus’s and PRAC’s implications for future systems and foreshadow future research directions. To aid future research, we open-source our Chronus implementation at https://github.com/CMU-SAFARI/Chronus.

Read disturbance in modern DRAM chips is a widespread weakness that is used for breaking memory isolation, one of the fundamental building blocks of system security and privacy. RowHammer is a prime example of read disturbance in DRAM where repeatedly accessing (hammering) a row of DRAM cells (DRAM row) induces bitflips in physically nearby DRAM rows (victim rows). Unfortunately, shrinking technology node size exacerbates RowHammer and as such, significantly fewer accesses can induce bitflips in newer DRAM chip generations. To ensure robust DRAM operation, state-of-the-art mitigation mechanisms restore the charge in potential victim rows (i.e., they perform preventive refresh or charge restoration). With newer DRAM chip generations, these mechanisms perform preventive refresh more aggressively and cause larger performance, energy, or area overheads. Therefore, it is essential to develop a better understanding and in-depth insights into the preventive refresh to secure real DRAM chips at low cost. In this paper, our goal is to mitigate RowHammer at low cost by understanding the preventive refresh latency and the impact of reduced refresh latency on RowHammer. To this end, we present the first rigorous experimental study on the interactions between refresh latency and RowHammer characteristics in real DRAM chips. Our experimental characterization using 388 real DDR4 DRAM chips from three major manufacturers demonstrates that a preventive refresh latency can be significantly reduced (by 64%) at the expense of requiring slightly more (by 0.54%) preventive refreshes. To investigate the impact of reduced preventive refresh latency on system performance and energy efficiency, we reduce the preventive refresh latency and adjust the aggressiveness of existing RowHammer solutions by developing a new mechanism, Partial Charge Restoration for Aggressive Mitigation (PaCRAM). Our results show that by reducing the preventive refresh latency, PaCRAM reduces the performance and energy overheads induced by five state-of-the-art RowHammer mitigation mechanisms with small additional area overhead. Thus, PaCRAM introduces a novel perspective into addressing RowHammer vulnerability at low cost by leveraging our experimental observations. To aid future research, we open-source our PaCRAM implementation at https://github.com/CMU-SAFARI/PaCRAM.

Genome sequence analysis is fundamental to medical breakthroughs such as developing vaccines, enabling genome editing, and facilitating personalized medicine. The exponentially expanding sequencing datasets and complexity of sequencing algorithms necessitate performance enhancements. While the performance of software solutions is constrained by their underlying hardware platforms, the utility of fixed-function accelerators is restricted to only certain sequencing algorithms.This paper presents QUETZAL, the first general-purpose vector acceleration framework designed for high efficiency and broad applicability across a diverse set of genomics algorithms. While a commercial CPU’s vector datapath is a promising candidate to exploit the data-level parallelism in genomics algorithms, our analysis finds that its performance is often limited due to long-latency scatter/gather memory instructions. QUETZAL introduces a hardware-software co-design comprising an accelerator microarchitecture closely integrated with the CPU’s vector datapath, alongside novel vector instructions to fully capitalize on the proposed hardware. QUETZAL integrates a set of scratchpad-style buffers meticulously designed to minimize latency associated with scatter/gather instructions during the retrieval of input genome sequences data. QUETZAL supports both short and long reads, and different types of sequencing data formats. A combination of hardware and software techniques enables QUETZAL to reduce the latency of memory instructions, perform complex computation using a single instruction, and transform data representations at runtime, resulting in overall efficiency gain. QUETZAL significantly accelerates a vectorized CPU baseline on modern genome sequence analysis algorithms by 5.7×, while incurring a small area overhead of 1.4% post place-and-route at the 7nm technology node compared to an HPC ARM CPU.

RowHammer is a major read disturbance mechanism in DRAM where repeatedly accessing (hammering) a row of DRAM cells (DRAM row) induces bitflips in other physically nearby DRAM rows. RowHammer solutions perform preventive actions (e.g., refresh neighbor rows of the hammered row) that mitigate such bitflips to preserve memory isolation, a fundamental building block of security and privacy in modern computing systems. However, preventive actions induce non-negligible memory request latency and system performance overheads as they interfere with memory requests. As shrinking technology node size over DRAM chip generations exacerbates RowHammer, the overheads of RowHammer solutions become prohibitively expensive. As a result, a malicious program can effectively hog the memory system and deny service to benign applications by causing many RowHammer-preventive actions. In this work, we tackle the performance overheads of RowHammer solutions by tracking and throttling the generators of memory accesses that trigger RowHammer solutions. To this end, we propose BreakHammer. BreakHammer 1) observes the time-consuming RowHammer-preventive actions of existing RowHammer mitigation mechanisms, 2) identifies hardware threads that trigger many of these actions, and 3) reduces the memory bandwidth usage of each identified thread. As such, BreakHammer significantly reduces the number of RowHammer-preventive actions performed, thereby improving 1) system performance and DRAM energy, and 2) reducing the maximum slowdown induced on a benign application, with near-zero area overhead. Our extensive evaluations demonstrate that BreakHammer effectively reduces the negative performance, energy, and fairness effects of eight RowHammer mitigation mechanisms. To foster further research we open-source our BreakHammer implementation and scripts at https://github.com/CMU-SAFARI/BreakHammer.

Modern computing systems access data in main memory at coarse granularity (e.g., at 512-bit cache block granularity). Coarse-grained access leads to wasted energy because the system does not use all individually accessed small portions (e.g., words , each of which typically is 64 bits) of a cache block. In modern DRAM-based computing systems, two key coarse-grained access mechanisms lead to wasted energy: large and fixed-size (i) data transfers between DRAM and the memory controller and (ii) DRAM row activations. We propose Sectored DRAM, a new, low-overhead DRAM substrate that reduces wasted energy by enabling fine-grained DRAM data transfer and DRAM row activation. To retrieve only useful data from DRAM, Sectored DRAM exploits the observation that many cache blocks are not fully utilized in many workloads due to poor spatial locality. Sectored DRAM predicts the words in a cache block that will likely be accessed during the cache block’s residency in cache and (i) transfers only the predicted words on the memory channel by dynamically tailoring the DRAM data transfer size for the workload and (ii) activates a smaller set of cells that contain the predicted words by carefully operating physically isolated portions of DRAM rows (i.e., mats). Activating a smaller set of cells on each access relaxes DRAM power delivery constraints and allows the memory controller to schedule DRAM accesses faster. We evaluate Sectored DRAM using 41 workloads from widely used benchmark suites. Compared to a system with coarse-grained DRAM, Sectored DRAM reduces the DRAM energy consumption of highly memory intensive workloads by up to (on average) 33% (20%) while improving their performance by up to (on average) 36% (17%). Sectored DRAM’s DRAM energy savings, combined with its system performance improvement, allows system-wide energy savings of up to 23%. Sectored DRAM’s DRAM chip area overhead is 1.7% of the area of a modern DDR4 chip. Compared to state-of-the-art fine-grained DRAM architectures, Sectored DRAM greatly reduces DRAM energy consumption, does not reduce DRAM bandwidth, and can be implemented with low hardware cost. Sectored DRAM provides 89% of the performance benefits of, consumes 12% less DRAM energy than, and takes up 34% less DRAM chip area than a high-performance state-of-the-art fine-grained DRAM architecture (Half-DRAM). It is our hope and belief that Sectored DRAM’s ideas and results will help to enable more efficient and high-performance memory systems. To this end, we open source Sectored DRAM at https://github.com/CMU-SAFARI/Sectored-DRAM.

Instruction injections or soft errors during execution on the CPU can cause serious system vulnerabilities. During the standard program flow of the processor, the injection of unauthorized instruction or the occurrence of an error in the expected instruction are the main conditions for potentially serious such vulnerabilities. With the execution of these unauthorized instructions, adversaries could exploit SoC and execute their own malicious program or get higher-level privileges on the system. On the other hand, non-intentional errors can potentially corrupt programs causing unintended executions or the cause of program crashes. Modern trusted architectures propose solutions for unauthorized execution on SoC with additional software mechanisms or extra hardware logic on the same untrusted SoC. Nevertheless, these SoCs can still be vulnerable, as long as deployed security detection mechanisms are embedded within the same SoC’s fabric. Furthermore, validation mechanisms on the SoC increase the complexity and power consumption of the SoC. This paper presents DEV-PIM, a new, high-performance, and low-cost execution validation mechanism in SoCs with external DRAM memory. The proposed approach uses processing-in-memory (PIM) method to detect instruction injections or corrupted instructions by utilising basic computing resources on a standard DRAM device. DEV-PIM transfers instructions scheduled for execution on the CPU to the DRAM and validates them by comparing content with the trusted program record on the DRAM using PIM operations. By optimising the DRAM scheduling process validation tasks are only executed when memory access is idle. The CPU retains uninterrupted memory access and can continue its normal program flow without penalty. We evaluate DEV-PIM in an end-to-end DRAM-compatible environment and run a set of software benchmarks. On average, the proposed architecture is able to detect 98.46% of instruction injections for different validation. We also measured on average only 0.346% CPU execution overhead with DEV-PIM enabled.

Commodity DRAM-based processing-using-memory (PuM) techniques that are supported by off-the-shelf DRAM chips present an opportunity for alleviating the data movement bottleneck at low cost. However, system integration of these techniques imposes non-trivial challenges that are yet to be solve d . Potential solutions to the integration challenges require appropriate tools to develop any necessary hardware and software components. Unfortunately, current proprietary computing systems, specialized DRAM-testing platforms, or system simulators do not provide the flexibility and/or the holistic system view that is necessary to properly evaluate and deal with the integration challenges of commodity DRAM-based PuM techniques. We design and develop Processing-in-DRAM (PiDRAM), the first flexible end-to-end framework that enables system integration studies and evaluation of real, commodity DRAM-based PuM techniques. PiDRAM provides software and hardware components to rapidly integrate PuM techniques across the whole system software and hardware stack. We implement PiDRAM on an FPGA-based RISC-V system. To demonstrate the flexibility and ease of use of PiDRAM, we implement and evaluate two state-of-the-art commodity DRAM-based PuM techniques: (i) in-DRAM copy and initialization (RowClone) and (ii) in-DRAM true random number generation (D-RaNGe) . We describe how we solve key integration challenges to make such techniques work and be effective on a real-system prototype, including memory allocation, alignment, and coherence. We observe that end-to-end RowClone speeds up bulk copy and initialization operations by 14.6× and 12.6×, respectively, over conventional CPU copy, even when coherence is supported with inefficient cache flush operations. Over PiDRAM’s extensible codebase, integrating both RowClone and D-RaNGe end-to-end on a real RISC-V system prototype takes only 388 lines of Verilog code and 643 lines of C++ code.

To understand and improve DRAM performance, reliability, security, and energy efficiency, prior works study characteristics of commodity DRAM chips. Unfortunately, state-of-the-art open source infrastructures capable of conducting such studies are obsolete, poorly supported, or difficult to use, or their inflexibility limits the types of studies they can conduct. We propose DRAM Bender, a new FPGA-based infrastructure that enables experimental studies on state-of-the-art DRAM chips. DRAM Bender offers three key features at the same time. First, DRAM Bender enables directly interfacing with a DRAM chip through its low-level interface. This allows users to issue DRAM commands in arbitrary order and with finer-grained time intervals compared to other open source infrastructures. Second, DRAM Bender exposes easy-to-use C++ and Python programm ing interfaces, allowing users to quickly and easily develop different types of DRAM experiments. Third, DRAM Bender is easily extensible. The modular design of DRAM Bender allows extending it to: 1) support existing and emerging DRAM interfaces and 2) run on new commercial or custom FPGA boards with little effort. To demonstrate that DRAM Bender is a versatile infrastructure, we conduct three case studies, two of which lead to new observations about the DRAM RowHammer vulnerability. In particular, we show that data patterns supported by DRAM Bender uncover a larger set of bit-flips on a victim row than those commonly used by prior work. We demonstrate the extensibility of DRAM Bender by implementing it on five different FPGAs with DDR4 and DDR3 support. DRAM Bender is freely and openly available athttps://github.com/CMU-SAFARI/DRAM-Bender.

Modern cloud computing systems distribute software executables over a network to keep the software sources, which are typically compiled in a security-critical cluster, secret. However, these executables are still vulnerable to reverse engineering techniques that can extract secret information from programs (e.g., an algorithm, cryptographic keys), violating the IP rights and potentially exposing the trade secrets of the software developer. Malicious parties can (i) statically analyze the disassembly of the executable (static analysis) or (ii) dynamically analyze the software by executing it on a controlled device and observe performance counter values or exploit side-channels to reverse engineer software (dynamic analysis).We develop ERIC, a new, efficient, and general software obfuscation framework. ERIC protects software against (i) static analysis, by making only an encrypted version of software executables available to the human eye, no matter how the software is distributed, and (ii) dynamic analysis, by guaranteeing that an encrypted executable can only be correctly decrypted and executed by a single authenticated device. ERIC comprises key hardware and software components to provide efficient software obfuscation support: (i) a hardware decryption engine (HDE) enables efficient decryption of encrypted hardware in the target device, (ii) the compiler can seamlessly encrypt software executables given only a unique device identifier. Both the hardware and software components are ISA-independent, making ERIC general. The key idea of ERIC is to use physical unclonable functions (PUFs), unique device identifiers, as secret keys in encrypting software executables. Malicious parties that cannot access the PUF in the target device cannot perform static or dynamic analyses on the encrypted binary.We develop ERIC’s prototype on an FPGA to evaluate it end-to-end. Our prototype extends RISC-V Rocket Chip with the hardware decryption engine (HDE) to minimize the overheads of software decryption. We augment the custom LLVM-based compiler to enable partial/full encryption of RISC-V executables. The HDE incurs minor FPGA resource overheads, it requires 2.63% more LUTs and 3.83% more flip-flops compared to the Rocket Chip baseline. LLVM-based software encryption increases compile time by 15.22% and the executable size by 1.59%. ERIC is publicly available and can be downloaded from https://github.com/kasirgalabs/ERIC.

Random number generation is an important task in a wide variety of critical applications including cryptographic algorithms, scientific simulations, and industrial testing tools. True Random Number Generators (TRNGs) produce cryptographically-secure truly random data by sampling a physical entropy source that typically requires custom hardware and suffers from long latency. To enable high-bandwidth and low-latency TRNGs on widely-available commodity devices, recent works propose hardware TRNGs that generate random numbers using commodity DRAM as an entropy source. Although prior works demonstrate promising TRNG mechanisms using DRAM, practical integration of such mechanisms into real systems poses various challenges.We identify three key challenges for using DRAM-based TRNGs in current systems: (1) generating random numbers with DRAM-based TRNGs can degrade overall system performance by slowing down concurrently-running applications due to the interference between RNG and regular memory operations in the memory controller (i.e., RNG interference), (2) this RNG interference can degrade system fairness by causing unfair prioritization of applications that intensively use random numbers (i.e., RNG applications), and (3) RNG applications can experience significant slowdown due to the high latency of DRAM-based TRNGs.To address these challenges, we propose DR-STRaNGe, an end-to-end system design for DRAM-based TRNGs that (1) reduces the RNG interference by separating RNG requests from regular memory requests in the memory controller, (2) improves fairness across applications with an RNG-aware memory request scheduler, and (3) hides the large TRNG latencies using a random number buffering mechanism combined with a new DRAM idleness predictor that accurately identifies idle DRAM periods.We evaluate DR-STRaNGe using a comprehensive set of 186 multi-programmed workloads. Compared to an RNG-oblivious baseline system, DR-STRaNGe improves the performance of non-RNG and RNG applications on average by 17.9% and 25.1%, respectively. DR-STRaNGe improves system fairness by 32.1% on average when generating random numbers at a 5 Gb/s throughput. DR-STRaNGe reduces energy consumption by 21% compared to the RNG-oblivious baseline design by reducing the time spent for RNG and non-RNG memory accesses by 15.8%.

DRAM is the building block of modern main memory systems. DRAM cells must be periodically refreshed to prevent data loss. Refresh operations degrade system performance by interfering with memory accesses. As DRAM chip density increases with technology node scaling, refresh operations also increase be-cause: 1) the number of DRAM rows in a chip increases; and 2) DRAM cells need additional refresh operations to mitigate bit failures caused by RowHammer, a failure mechanism that becomes worse with technology node scaling. Thus, it is critical to enable refresh operations at low performance overhead. To this end, we propose a new operation, Hidden Row Activation (HiRA), and the HiRA Memory Controller (HiRA-MC) to perform HiRA operations. HiRA hides a refresh operation’s latency by refreshing a row concurrently with accessing or refreshing another row within the same bank. Unlike prior works, HiRA achieves this parallelism without any modifications to off-the-shelf DRAM chips. To do so, it leverages the new observation that two rows in the same bank can be activated without data loss if the rows are connected to different charge restoration circuitry. We experimentally demonstrate on 56 real off-the-shelf DRAM chips that HiRA can reliably parallelize a DRAM row’s refresh operation with refresh or activation of any of the 32% of the rows within the same bank. By doing so, HiRA reduces the overall latency of two refresh operations by 51.4%. HiRA-MC modifies the memory request scheduler to perform HiRA when a refresh operation can be performed concurrently with a memory access or another refresh. Our system-level evaluations show that HiRA-MC increases system performance by 12.6% and $3.73\times$ as it reduces the performance degradation due to periodic refreshes and refreshes for RowHammer protection (preventive refreshes), respectively, for future DRAM chips with increased density and RowHammer vulnerability.

On-chip memory (usually based on Static RAMs—SRAMs) are crucial components for various computing devices including heterogeneous devices, e.g., GPUs, FPGAs, and ASICs, to achieve high performance. Modern workloads such as deep neural networks (DNNs) running on these heterogeneous fabrics are highly dependent on the on-chip memory architecture for efficient acceleration. Hence, improving the energy efficiency of such memories directly leads to an efficient system. One of the common methods to save energy is undervolting, i.e., supply voltage underscaling below the nominal level. Such systems can be safely undervolted without incurring faults down to a certain voltage limit. This safe range is also called voltage guardband. However, reducing voltage below the guardband level without decreasing frequency causes timing-based faults. In this article, we propose MoRS, a framework that generates the first approximate undervolting fault model using real faults extracted from experimental undervolting studies on SRAMs to build the model. We inject the faults generated by MoRS into the on-chip memory of the DNN accelerator to evaluate the resilience of the system under the test. MoRS has the advantage of simplicity without any need for high-time overhead experiments while being accurate enough in comparison to a fully randomly generated fault injection approach. We evaluate our experiment in popular DNN workloads by mapping weights to SRAMs and measure the accuracy difference between the output of the MoRS and the real data. Our results show that the maximum difference between real fault data and the output fault model of MoRS is 6.21%, whereas the maximum difference between real data and random fault injection model is 23.2%. In terms of average proximity to the real data, the output of MoRS outperforms the random fault injection approach by$3.21\times $.

The continuous quest for performance pushed processors to incorporate elements such as multiple cores, caches, acceleration units, or speculative execution that make systems very complex. On the other hand, these features often expose unexpected vulnerabilities that pose new challenges. For example, the timing differences introduced by caches or speculative execution can be exploited to leak information or detect activity patterns. Protecting embedded systems from existing attacks is extremely challenging, and it is made even harder by the continuous rise of new microarchitectural attacks (e.g., the Spectre and Orchestration attacks). In this article, we present a new approach based on count-min sketches for detecting microarchitectural attacks in the microprocessors featured by embedded systems. The idea is to add to the system a security checking module (without modifying the microprocessor under protection) in charge of observing the fetched instructions and identifying and signaling possible suspicious activities without interfering with the nominal activity of the system. The proposed approach can be programmed at design time (and reprogrammed after deployment) in order to always keep updated the list of the attacks that the checker is able to identify. We integrated the proposed approach in a large RISC-V core, and we proved its effectiveness in detecting several versions of the Spectre, Orchestration, Rowhammer, and Flush + Reload attacks. In its best configuration, the proposed approach has been able to detect 100% of the attacks, with no false alarms and introducing about 10% area overhead, about 4% power increase, and without working frequency reduction.

True random number generators (TRNG) sample random physical processes to create large amounts of random numbers for various use cases, including security-critical cryptographic primitives, scientific simulations, machine learning applications, and even recreational entertainment. Unfortunately, not every computing system is equipped with dedicated TRNG hardware, limiting the application space and security guarantees for such systems. To open the application space and enable security guarantees for the overwhelming majority of computing systems that do not necessarily have dedicated TRNG hardware (e.g., processing-in-memory systems), we develop QUAC-TRNG, a new high-throughput TRNG that can be fully implemented in commodity DRAM chips, which are key components in most modern systems.QUAC-TRNG exploits the new observation that a carefully-engineered sequence of DRAM commands activates four consecutive DRAM rows in rapid succession. This QUadruple ACtivation (QUAC) causes the bitline sense amplifiers to non-deterministically converge to random values when we activate four rows that store conflicting data because the net deviation in bitline voltage fails to meet reliable sensing margins.We experimentally demonstrate that QUAC reliably generates random values across 136 commodity DDR4 DRAM chips from one major DRAM manufacturer. We describe how to develop an effective TRNG (QUAC-TRNG) based on QUAC. We evaluate the quality of our TRNG using the commonly-used NIST statistical test suite for randomness and find that QUAC-TRNG successfully passes each test. Our experimental evaluations show that QUAC-TRNG reliably generates true random numbers with a throughput of 3.44 Gb/s (per DRAM channel), outperforming the state-of-the-art DRAM-based TRNG by 15.08× and 1.41× for basic and throughput-optimized versions, respectively. We show that QUAC-TRNG utilizes DRAM bandwidth better than the state-of-the-art, achieving up to 2.03× the throughput of a throughput-optimized baseline when scaling bus frequencies to 12 GT/s.

We empirically evaluate an undervolting technique, i.e., underscaling the circuit supply voltage below the nominal level, to improve the power-efficiency of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing faults due to excessive circuit latency increase. We evaluate the reliability-power trade-off for such accelerators. Specifically, we experimentally study the reduced-voltage operation of multiple components of real FPGAs, characterize the corresponding reliability behavior of CNN accelerators, propose techniques to minimize the drawbacks of reduced-voltage operation, and combine undervolting with architectural CNN optimization techniques, i.e., quantization and pruning. We investigate the effect ofenvironmental temperature on the reliability-power trade-off of such accelerators. We perform experiments on three identical samples of modern Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification CNN benchmarks. This approach allows us to study the effects of our undervolting technique for both software and hardware variability. We achieve more than 3X power-efficiency (GOPs/W ) gain via undervolting. 2.6X of this gain is the result of eliminating the voltage guardband region, i.e., the safe voltage region below the nominal level that is set by FPGA vendor to ensure correct functionality in worst-case environmental and circuit conditions. 43% of the power-efficiency gain is due to further undervolting below the guardband, which comes at the cost of accuracy loss in the CNN accelerator. We evaluate an effective frequency underscaling technique that prevents this accuracy loss, and find that it reduces the power-efficiency gain from 43% to 25%.

DRAM is the primary technology used for main memory in modern systems. Unfortunately, as DRAM scales down to smaller technology nodes, it faces key challenges in both data integrity and latency, which strongly affects overall system reliability and performance. To develop reliable and high-performance DRAM-based main memory in future systems, it is critical to characterize, understand, and analyze various aspects (e.g., reliability, latency) of existing DRAM chips. To enable this, there is a strong need for a publicly-available DRAM testing infrastructure that can flexibly and efficiently test DRAM chips in a manner accessible to both software and hardware developers. This paper develops the first such infrastructure, SoftMC (Soft Memory Controller), an FPGA-based testing platform that can control and test memory modules designed for the commonly used DDR (Double Data Rate) interface. SoftMC has two key properties: (i) it provides flexibility to thoroughly control memory behavior or to implement a wide range of mechanisms using DDR commands; and (ii) it is easy to use as it provides a simple and intuitive high-level programming interface for users, completely hiding the low-level details of the FPGA. We demonstrate the capability, flexibility, and programming ease of SoftMC with two example use cases. First, we implement a test that characterizes the retention time of DRAM cells. Experimental results we obtain using SoftMC are consistent with the findings of prior studies on retention time in modern DRAM, which serves as a validation of our infrastructure. Second, we validate two recently-proposed mechanisms, which rely on accessing recently-refreshed or recently-accessed DRAM cells faster than other DRAM cells. Using our infrastructure, we show that the expected latency reduction effect of these mechanisms is not observable in existing DRAM chips, which demonstrates the usefulness of SoftMC in testing new ideas on existing memory modules. We discuss several other use cases of SoftMC, including the ability to characterize emerging non-volatile memory modules that obey the DDR standard. We hope that our open-source release of SoftMC fills a gap in the space of publicly-available experimental memory testing infrastructures and inspires new studies, ideas, and methodologies in memory system design.

MOTIVATION: High throughput DNA sequencing (HTS) technologies generate an excessive number of small DNA segments -called short reads- that cause significant computational burden. To analyze the entire genome, each of the billions of short reads must be mapped to a reference genome based on the similarity between a read and 'candidate' locations in that reference genome. The similarity measurement, called alignment, formulated as an approximate string matching problem, is the computational bottleneck because: (i) it is implemented using quadratic-time dynamic programming algorithms and (ii) the majority of candidate locations in the reference genome do not align with a given read due to high dissimilarity. Calculating the alignment of such incorrect candidate locations consumes an overwhelming majority of a modern read mapper's execution time. Therefore, it is crucial to develop a fast and effective filter that can detect incorrect candidate locations and eliminate them before invoking computationally costly alignment algorithms. RESULTS: We propose GateKeeper, a new hardware accelerator that functions as a pre-alignment step that quickly filters out most incorrect candidate locations. GateKeeper is the first design to accelerate pre-alignment using Field-Programmable Gate Arrays (FPGAs), which can perform pre-alignment much faster than software. When implemented on a single FPGA chip, GateKeeper maintains high accuracy (on average >96%) while providing, on average, 90-fold and 130-fold speedup over the state-of-the-art software pre-alignment techniques, Adjacency Filter and Shifted Hamming Distance (SHD), respectively. The addition of GateKeeper as a pre-alignment step can reduce the verification time of the mrFAST mapper by a factor of 10. AVAILABILITY AND IMPLEMENTATION: https://github.com/BilkentCompGen/GateKeeper. CONTACT: [email protected] or [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DRAM latency continues to be a critical bottleneck for system performance. In this work, we develop a low-cost mechanism, called Charge Cache, that enables faster access to recently-accessed rows in DRAM, with no modifications to DRAM chips. Our mechanism is based on the key observation that a recently-accessed row has more charge and thus the following access to the same row can be performed faster. To exploit this observation, we propose to track the addresses of recently-accessed rows in a table in the memory controller. If a later DRAM request hits in that table, the memory controller uses lower timing parameters, leading to reduced DRAM latency. Row addresses are removed from the table after a specified duration to ensure rows that have leaked too much charge are not accessed with lower latency. We evaluate ChargeCache on a wide variety of workloads and show that it provides significant performance and energy benefits for both single-core and multi-core systems.

Scaling supply voltage to near-threshold is a very effective approach in reducing the energy consumption of computer systems. However, executing below the safe operation margin of supply voltage introduces high number of persistent failures, especially in memory structures. Thus, it is essential to provide reliability schemes to tolerate these persistent failures in the memory structures. In this study, we adopt a Single Error Correction Multiple Adjacent Error Correction (SEC-MAEC) code in order to minimize the energy consumption of L1 caches. In our evaluations, we present that the SEC-MAEC code is a fast and energy efficient Error Correcting Code (ECC). It presents 10X less area overhead and 2X less latency for the decoder compared to Orthogonal Latin Square Code, the state-of-the art ECC utilized in the L1 cache under the scaling supply voltage.

Fault tolerance has become a fundamental concern in computer design, in addition to performance and power. Although several error detection schemes have been proposed to discover a faulty core in the system, these proposals could waste the whole core, including many error-free structures in it after error detection. Moreover, many fault-tolerant designs require additional hardware for data replication or for comparing the replicated data. In this study, we provide a low-cost, fine-grained error detection scheme by exploiting already existing comparators and data replications in the several pipeline stages such as issue queue, rename logic, and translation lookaside buffer. We reduce the vulnerability of the source register tags in IQ by 60%, the vulnerability of instruction TLB by 64%, the vulnerability of data TLB by 45%, and the vulnerability of the register tags of rename logic by 20%.

SRAM arrays are used especially in memory structures inside the processor. Static energy dissipation caused by leakage currents is increasing with every new technology and large SRAM arrays are the main source of the leakage current. We analyzed the content distribution of columns of SRAM arrays and based on the majority of the content, the body-bias of transistors are changed to reduce the static energy dissipation of these SRAM arrays. Our simulations reveal that when our technique is used in the register file of the processor, the leakage energy dissipation decreases by 39% and the total energy dissipation by 14% with an area overhead of 11%.

Soft errors caused by cosmic rays or alpha particles emitted from the packaging material around the chips are becoming an increasingly important challenge in reliable microprocessor design. Transistor density, and die size trends show that soft errors will gain even more importance in the future. Due to their significant overheads, most redundancy schemes are employed where the penalty incurred can be hidden in the pipeline. Most contemporary processors employ a large physical register file to hold the produced results which may reside there for a long time. The register file is a critical element of a microprocessor and is needed to be protected against soft errors. In this paper we propose an SRAM bitcell design with ability to hold a redundant copy of the data and compare the copies with built-in comparators to detect a possible mismatch. Our experimental results show that the proposed design has 34% area, 7.9% power 2% delay overheads which are further reducible and can protect the register file against Silent Data Corruption (SDC).

Today, designing reliable superscalar microprocessors is more complex and challenging than it has always been. Increasing current densities over interconnecting wires, whose dimensions are continuously scaling down, has been an important research problem. Performance and power efficiency concerns also lead to more complex structures. The complexity and the wire densities of the blocks in smaller areas make them more prone to failures and malfunctions caused by the "electromigration" problem. Most contemporary processors employ wide pipelines that are able to execute multiple instructions each cycle. Because of some limiting factors such as the dependencies and input/output latencies, pipelines are rarely used up to their full capacity, which causes some ports to be used more and worn out earlier. This paper proposes a method to help preventing chips against the corruptive effect of electromigration by distributing the utilization evenly between ports. Furthermore, in data holding structures like register files, bit level inactivity is exploited to provide further reliability improvements. This is accomplished by changing the flux density on wires through exchanging the positions of significant bits of values and changing the widths of bit line wires according to their level of usage.

Static energy dissipation is an increasing problem in contemporary processor design with shrinking feature sizes. Many schemes are proposed to cope with leakage in the literature ranging from using sleep transistors to lowering supply voltage. In this paper, we introduce a Conscious SRAM (CSRAM) design to lower static energy dissipation in the storage components of a processor. The proposed bitcell design adapts the body bias of its own transistors according to its contents. We show that the use of the proposed CSRAM cells results in significant reduction in the static energy dissipation of on-chip storage components without significant performance degradation. In order to reduce the area overhead introduced by the CSRAM we propose a simplified version of the cell at the circuit level. We also leverage the fact that the contents of adjacent bits of the stored values are highly dependent on each other, especially on the upper order bits of a value, and propose some architectural level solutions that lower the area overhead to as low as 7%.

Register file is one of the vital and energy consuming parts inside microprocessor. Many studies show that it is one of the hot spots on the chip. It is also observed by many researchers that many of the produced values in a processor are narrow. By using the narrow values, register files can store fewer bits and may be designed to need less static and dynamic energy. In this paper we propose a register file design that stores data in narrow value groups and values are written to those groups according to their widths. Size of narrow value groups can be set dynamically according to the behavior of the program while having the same performance. We show that the register file which has dynamically changing narrow value groups offers static and dynamic energy savings in the register file up to 65% with negligible performance loss.

Soft errors caused by cosmic particles and radiation emitted by the packaging are an important problem in contemporary micropro-cessors. Parity bits are used to detect single bit errors that occur in the storage components. In order to implement parity logic, multiple levels of XOR gates are used and these XOR trees are known to have high delay. Many produced and consumed values inside a processor hold consecutive zeros and ones in their upper order bits. These values can be represented with less number of bits and hence are termed narrow. In this paper we propose a parity generator circuit design that is capable of generating the parity if the input value is narrow. We show that parity can be generated faster than a regular XOR tree implementation using our design for the values that can be represented using fewer bits.

Register pressure in modern superscalar processors can be reduced by releasing registers early and by copying their contents to cheap back-up storage. This article quantifies the potential benefits of register occupancy reduction and shows that existing hardware-based schemes typically achieve only a small fraction of this potential. This is because they are unable to accurately determine the last use of a register and must wait until the redefining instruction enters the pipeline. On the other hand, compilers have a global view of the program and, using simple dataflow analysis, can determine the last use. This article evaluates the extent to which compiler analysis can aid early releasing, explores the design space, and introduces commit and issue-based early releasing schemes, quantifying their benefits. Using simple compiler analysis and microarchitecture changes, we achieve 70% of the potential register file occupancy reduction. By adding more hardware support, we can increase this to 94%. Our schemes are compared to state-of-the-art approaches for varying register file sizes and are shown to outperform these existing techniques.

The register file is a critical component in a modern superscalar processor. It must be large enough to accommodate the results of all in-flight instructions. It must also have enough ports to allow simultaneous issue and writeback of many values each cycle. However, this makes it one of the most energy-consuming structures within the processor with a high access latency. As technology scales, there comes a point where register accesses are the bottleneck to performance and so must be pipelined over several cycles. This increases the pipeline depth, lowering performance. To overcome these challenges, we propose a novel use of compiler analysis to aid register caching. Adding a register cache allows us to preserve single-cycle register accesses, maintaining performance and reducing energy consumption. We do this by passing information to the processor using free bits in a real ISA, allowing us to cache only the most important registers. Evaluating the register cache over a variety of sizes and associativities and varying the read ports into the cache, our best scheme achieves an energy-delay-squared (EDD) product of 0.81, with a performance increase of 11%. Another configuration saves 13% of register system energy. Using four register cache read ports brings both performance gains and energy savings, consistently outperforming two state-of-the-art hardware approaches.

Soft errors are an important challenge in contemporary microprocessors. Particle hits on the components of a processor are expected to create an increasing number of transient errors with each new microprocessor generation. In this paper, we propose simple mechanisms that effectively reduce the vulnerability to soft errors in a processor. Our designs are generally motivated by the fact that many of the produced and consumed values in the processors are narrow and their upper order bits are meaningless. Soft errors caused by any particle strike to these higher order bits can be avoided by simply identifying these narrow values. Alternatively, soft errors can be detected or corrected on the narrow values by replicating the vulnerable portion of the value inside the storage space provided for the upper order bits of these operands. As a faster but less fault tolerant alternative to ECC and parity, we offer a variety of schemes that make use of narrow values and analyze their efficiency in reducing soft error vulnerability of different data-holding components of a processor. On average, techniques that make use of the narrowness of the values can provide 49 percent error detection, 45 percent error correction, or 27 percent error avoidance coverage for single bit upsets in the first level data cache across all Spec2K. In other structures such as the immediate field of the issue queue, an average error detection rate of 64 percent is achieved.

This paper proposes the fuse, a technique to anticipate failures due to degradation in any ALU (arithmetic logic unit), and particularly in an adder. The fuse consists of a replica of the weakest transistor in the adder and the circuitry required to measure its degradation. By mimicking the behavior of the replicated transistor the fuse anticipates the failure short before the first failure in the adder appears, and hence, data corruption and program crashes can be avoided. Our results show that the fuse anticipates the failure in more than 99.9% of the cases after 96.6% of the lifetime, even for pessimistic random within-die variations.

Narrow values that can be represented by less number of bits than the full machine width occur very frequently in programs. On the other hand, clustering mechanisms enable cost- and performance-effective scaling of processor back-end features. Those attributes can be combined synergistically to design special clusters operating on narrow values (a.k.a. helper cluster), potentially providing performance benefits. We complement a 32-bit monolithic processor with a low-complexity 8-bit helper cluster. Then, in our main focus, we propose various ideas to select suitable instructions to execute in the data-width based clusters. We add data-width information as another instruction steering decision metric and introduce new data-width based selection algorithms which also consider dependency, inter-cluster communication and load imbalance. Utilizing those techniques, the performance of a wide range of workloads are substantially increased; helper cluster achieves an average speedup of 11% for a wide range of 412 apps. When focusing on integer applications, the speedup can be as high as 22% on average

Traditional dynamic scheduler designs use one issue queue entry per instruction, regardless of the actual number of operands actively involved in the wakeup process. We propose Instruction Packing---a novel microarchitectural technique that reduces both delay and power consumption of the issue queue by sharing the associative part of an issue queue entry between two instructions, each with, at most, one nonready register source operand at the time of dispatch. Our results show that this technique results in 40% reduction of the IQ power and 14% reduction in scheduling delay with negligible IPC degradations.

Modern superscalar microprocessors need sizable register files to support a large number of in-flight instructions for exploiting instruction level parallelism (ILP). An alternative to building large register files is to use a smaller number of registers, but manage them more effectively. More efficient management of registers can also result in higher performance if the reduction of the register file size is not the goal. Traditional register file management mechanisms deallocate a physical register only when the next instruction writing to the same destination architectural register commits. In this paper, we propose several techniques for deallocating physical registers much earlier. Our designs rely on the use of a checkpointed register file (CRF), where a local shadow copy of each bitcell is used to temporarily save the values of the early deallocated registers should they be needed to recover from branch mispredictions or to reconstruct the precise state after exceptions or interrupts. The proposed techniques try to release registers as soon as possible and are more aggressive than the previously proposed schemes for early deallocation of registers

The dynamic instruction scheduling logic is one of the most critical components of modern superscalar microprocessors, both from the delay and power dissipation standpoints. The delay and energy requirement of driving the wakeup tags across the associatively-addressed issue queue accounts for a significant percentage of the scheduler's overhead and also limits the design scalability. We propose tag memoization and tagline folding - two schemes to reduce the power of wakeup tag broadcasts by reducing the number of tag-bits that are driven in each broadcast. Our results show that the combination of these mechanisms provides 223% average reduction of the wakeup tag broadcast power with no impact on the IPC.

The instruction scheduling logic used in modern superscalar microprocessors often relies on associative searching of the issue queue entries to dynamically wakeup instructions for the execution. Traditional designs use one issue queue entry for each instruction, regardless of the actual number of operands actively used in the wakeup process. In this paper we propose Instruction Packing - a novel microarchitectural technique that reduces both the delay and the power consumption of the issue queue by sharing the associative part of an issue queue entry between two instructions, each with at most one non-ready register source operand at the time of dispatch. Our results show that Instruction Packing provides a 39% reduction of the whole issue queue power and 21.6% reduction in the wakeup delay with as little as 0.4% IPC degradation on the average across the simulated SPEC benchmarks

A larger dynamic scheduler (DS) exposes more instruction level parallelism (ILP), giving better performance. However, a larger DS also results in a longer scheduler latency and a slower clock speed. In this paper, we propose a new DS design that reduces the scheduler critical path latency by reducing the wakeup width (defined as the effective number of results used for instruction wakeup). The design is based on the realization that the average number of results per cycle that are immediately required to wake up the dependent instructions is considerably less than the processor issue width. Our designs are evaluated using the simulation of the SPEC 2000 benchmarks and SPICE simulations of the actual issue queue layouts in 0.18 micron process. We found that a significant reduction in scheduler latency, power consumption and area is achieved with less than 2% reduction in the instructions per cycle (IPC) count for the SPEC2K benchmarks.

Modern superscalar microprocessors need sizable register files to support large number of in-flight instructions for exploiting ILP. An alternative to building large register files is to use smaller number of registers, but manage them more effectively. More efficient management of registers can also result in higher performance if the reduction of the register file size is not the goal. Traditional register file management mechanisms deallocate a physical register only when the next instruction with the same destination architectural register commits. We propose two complementary techniques for deallocating the register immediately after the instruction producing the register's value commits itself, without waiting for the commitment of the next instruction with the same destination. Our design relies on the use of a checkpointed register file (CRF), where a local shadow copy of each bitcell is used to temporarily save the early deallocated register values should they be needed to recover from branch mispredictions or to reconstruct the precise state after exceptions or interrupts. The proposed techniques outperform the previously proposed schemes for early deallocation of registers. For the register-constrained datapath configurations, our techniques result in up to 35% performance increase with 23.3% increase on the average across SPEC2000 benchmarks.

A large percentage of computed results have fewer significant bits compared to the full width of a register. We exploit this fact to pack multiple results into a single physical register to reduce the pressure on the register file in a superscalar processor. Two schemes for dynamically packing multiple "narrow-width" results into partitions within a single register are evaluated. The first scheme is conservative and allocates a full-width register for a computed result. If the computed result turns out to be narrow, the result is reallocated to partitions within a common register, freeing up the full-width register. The second scheme allocates register partitions based on a prediction of the width of the result and reallocates register partitions when the actual result width is higher than what was predicted. If the actual width is narrower than what was predicted, allocated partitions are freed up. A detailed evaluation of our schemes show that average IPC gains of up to 15% can be realized across the SPEC 2000 benchmarks on a somewhat register-constrained datapath.

All contemporary dynamically scheduled processors support register renaming to cope with false data dependencies. One of the ways to implement register renaming is to use the slots within the reorder buffer (ROB) as physical registers. In such designs, the ROB is a large multiported structure that occupies a significant portion of the die area and dissipates a sizable fraction of the total chip power. The heavily ported ROB is also likely to have a large delay that can limit the processor clock rate. We consider several approaches for reducing the ROB complexity in processors that use the ROB slots to implement physical registers. The first approach exploits the fact that the bulk of the source operand reads are satisfied through forwarding or reading of the committed register values. Our technique completely eliminates the read ports needed on the ROB for reading source operands. A small set of associatively addressed retention latches is used to compensate for the resulting performance degradation by caching the most recently produced results. The second technique relies on a distributed implementation that spreads the centralized ROB structure across the function units (FUs)', with each distributed component sized to match the FU workload and with one write port and two read ports on each component. The third approach combines the use of retention latches and a distributed ROB implementation that uses minimally ported distributed components. The net result of combining the two techniques is the ROB distribution with minimal conflicts over the read and no conflicts over the write ports. Our designs are evaluated using the simulation of SPEC 2000 benchmarks and measurements of the actual ROB layouts in a 0.18 micron CMOS process.

A mechanism for reducing the power requirements in processors that use a separate (architectural) register file (ARF) for holding committed values is proposed. We exploit the notion of short-lived operands-values that target architectural registers that are renamed by the time the instruction producing the value reaches the writeback stage. Our simulations of the SPEC 2000 benchmarks show that as much as 71 percent to 97 percent of the results are short-lived. Our technique avoids unnecessary writebacks into the result repository (a slot within the reorder buffer or a physical register) as well as writes into the ARF from unnecessary commitments by caching (and isolating) short-lived operands within a small dedicated register file. Operands are cached in this manner till they can be safely discarded without jeopardizing the recovery from possible branch mispredictions or reconstruction of the precise state in case of interrupts or exceptions. Additional energy savings are achieved by limiting the number of ports used for instruction commitment. The power/energy savings are validated using SPICE measurements of actual layouts in a 0.18 micron CMOS process. The energy reduction in the ROB and the ARF is about 20 percent (translating into the overall chip energy reduction of about 5 percent) and this is achieved with no increase in cycle time, little additional complexity, and no degradation in the number of instructions committed per cycle.

Modern superscalar datapaths use aggressive execution reordering to exploit instruction-level parallelism. Comparators, either explicit or embedded into content-addressable logic, are used extensively throughout such designs to implement several key out-of-order execution mechanisms and support the memory hierarchy. The traditional comparator designs dissipate energy on a mismatch in any bit position. As mismatches occur with a much higher frequency than matches in many situations, considerable improvements in energy dissipation are to be gained by using comparators that dissipate energy predominantly on a full match and little or no energy on partial or complete mismatches. We make two contributions. First, we introduce a series of dissipate-on-match comparator designs, including designs for comparing long arguments. Second, we show how comparators, used in modern datapaths, can be chosen and organized judiciously based on the microarchitectural-level statistics to minimize the energy dissipation. We use the actual layout data and the realistic bit patterns of the comparands (obtained from the simulated execution of SPEC 2000 benchmarks) to show the energy impact from the use of the new comparator designs. For the same delay, the proposed 8-bit comparators dissipate 70 percent less energy than the traditional designs if used within issue queues and 73 percent less energy if used within load-store queues. The use of the proposed 6-bit comparators within the dependency checking logic is shown to increase the energy dissipation by 65 percent on the average compared to the traditional designs. We also find that the use of a hybrid 32-bit comparator, comprised of three traditional 8-bit blocks and one proposed 8-bit block, is the most energy-efficient solution for the use in the load-store queue, resulting in 19 percent energy reduction compared to the use of four traditional 8-bit blocks used to implement a 32-bit comparator.

We consider two approaches for reducing the complexity and power dissipation in processors that use separate register file to maintain committed register values. The first approach relies on a distributed implementation of the reorder buffer (ROB) that spreads the centralized ROB structure across the function units (FUs), with each distributed component sized to match the FU workload and with one write port and two read ports on each component. The second approach combines the use of the previously proposed retention latches and a distributed ROB implementation that uses minimally-ported distributed components. Such a combination avoids any read and write port conflicts on the distributed ROB components (with the exception of possible port conflicts in the course of commitment) and does not incur the associated performance degradation. Our designs are evaluated using the simulation of the SPEC 2000 benchmarks and SPICE simulations of the actual ROB layouts in 0.18 micron process. The ROB power savings of up to 49% can be realized with only 1.7% performance loss on the average.

Modern superscalar processors implement precise interrupts by using the Reorder Buffer (ROB). In some microarchitectures , such as the Intel P6, the ROB also serves as a repository for the uncommitted results. In these designs, the ROB is a complex multi-ported structure that dissipates a significant percentage of the overall chip power. Recently, a mechanism was introduced for reducing the ROB complexity and its power dissipation through the complete elimination of read ports for reading out source operands. The resulting performance degradation is countered by caching the most recently produced results in a small set of associatively-addressed latches ("retention latches"). We propose an enhancement to the above technique by leveraging the notion of short-lived operands (values targeting the registers that are renamed by the time the instruction producing the value reaches the writeback stage). As much as 87% of all generated values are short lived for the SPEC 2000 benchmarks. Significant improvements in the utilization of retention latches, the overall performance, complexity and power are achieved by not caching short-lived values in the retention latches. As few as two retention latches allow all source operand read ports on the ROB to be completely eliminated with very little impact on performance.

Traditional pulldown comparators that are used to implement associative addressing logic in superscalar microprocessors dissipate energy on a mismatch in any bit position in the comparands. As mismatches occur much more frequently than matches in many situations, such circuits are extremely energy-inefficient. In recognition of this inefficiency, a series of dissipate-on-match comparator designs have been proposed to address the power considerations. These designs, however, are limited to at most 8-bit long arguments. In this paper, we examine the designs of energy-efficient comparators capable of comparing arguments as long as 32 bits in size. Such long comparands are routinely used in the load-store queues, caches, BTBs and TLBs. We use the actual layout data and the realistic bit patterns of the comparands (obtained from the simulated execution of SPEC 2000 benchmarks) to show the energy impact from the use of the new comparators. In general, a non-trivial combination of traditional and dissipate-on-match 8-bit comparator blocks represents the most energy-efficient and fastest solution. As an example of this general approach, we show how fast and energy-efficient comparators can be designed for comparing addresses within the load-store queue of a superscalar processor.

The out-of-order issue queue (IQ), used in modern superscalar processors is a considerable source of energy dissipation. We consider design alternatives that result in significant reductions in the power dissipation of the IQ (by as much as 75%) through the use of comparators that dissipate energy mainly on a tag match, 0-B encoding of operands to imply the presence of bytes with all zeros and, bitline segmentation. Our results are validated by the execution of SPEC 95 benchmarks on a true hardware level, cycle-by-cycle simulator for a superscalar processor and SPICE measurements for actual layouts of the IQ in a 0.18-/spl mu/m CMOS process.

Datapath components in modem high performance superscalar processors employ a significant amount of associative addressing logic based on the use of comparators that dissipate energy on a mismatch. These comparators are used to detect a full match, but as mismatches are much more common than full matches in some components of the CPU, considerable energy-inefficiencies occur within the associative logic. We propose the design of two new comparator circuits that predominantly dissipate energy on a match, thus resulting in very significant savings in comparator power dissipation. The proposed designs are evaluated using SPICE simulations of actual VLSI layouts of the comparators in 0.18 micron 6-metal layer process and micro-architectural level statistics.