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Vector-scalar comparison is a fundamental computation primitive that compares each element in a vector against a single scalar value. It is widely used in a broad range of data-intensive workloads from databases to machine learning. Due to its low computational intensity, the execution of this operation tends to be memory-bound, especially for large vectors, thereby limiting the utilization of compute resources. Processing-using-DRAM (PuD) is an emerging computing paradigm that performs massively parallel bitwise operations directly within the DRAM array, alleviating off-chip data movement. Unfortunately, no prior work proposes an efficient PuD-based solution tailored to vector-scalar comparisons. Existing PuD-based approaches require many DRAM commands because the comparison's algorithmic complexity grows with operand bit-width in the bit-serial execution model, which is inherently induced by current PuD architectures. As a result, this command overhead becomes the dominant performance bottleneck, limiting application-level speed up. We propose Clutch, a novel data representation and comparison algorithm for accelerating vector-scalar comparisons in PuD systems with high efficiency and scalability. Our key idea is twofold. First, to reduce the number of DRAM commands required for comparison, Clutch adopts temporal coding for vectors, where each value is encoded as a sequence of leading ones. This enables lookup-based comparisons, where comparing against a scalar input simply involves accessing the corresponding DRAM row. Second, Clutch leverages our key insight that a divide-and-conquer approach enables scalable lookup-based comparisons without incurring a prohibitive memory footprint at high bit-precision. Specifically, Clutch partitions the operand into multiple multi-bit chunks which can be compared independently using compact lookup tables, and merges per-chunk results through a procedure designed to execute efficiently on PuD.Clutch provides a flexible tradeoff between throughput and memory usage by adjusting chunk count. Experimental results on two applications, predicate evaluation and decision tree inference, demonstrate that Clutch improves end-to-end application throughput (and energy efficiency) by an average of 12 × (69 ×) over highly-optimized CPU and GPU execution and 2.9 × (3.0 ×) over the state-of-the-art bit-serial PuD implementation. Notably, we present, to our knowledge, the first mapping of decision tree inference to PuD execution, extending PuD to a new application domain. Our results demonstrate that DRAM can serve as a high-performance and energy-efficient computing substrate for comparison-intensive workloads.

A MICRO 2024 best paper runner-up publication (the Mess paper [1]) with all three artifact badges awarded (including “Reproducible”) proposes a new benchmark to evaluate real and simulated memory system performance. The publication contends that Ramulator 2.0 [2] and DAMOV [3] (ZSim+Ramulator) (along with other existing memory system simulators) “poorly resemble the actual system performance” and asserts that their simulator is better.In this paper, we show that the Mess paper [1] has 1) demonstrable technical misconfigurations, 2) methodological errors in interpreting simulation statistics, and 3) an incomplete artifact that makes its key results irreproducible. We demonstrate that the Ramulator 2.0 simulation results reported in [1] are incorrect due to multiple configuration errors instead of inherent simulation inaccuracy claimed by the Mess paper. We show that by correctly configuring Ramulator 2.0, Ramulator 2.0’s simulated memory system performance actually resembles real system characteristics well, and thus a key claimed contribution of [1] is factually incorrect. We also identify that the DAMOV simulation results in [1] use wrong simulation statistics that are unrelated to the simulated DRAM performance. By using correct DRAM simulation statistics, we show that DAMOV’s simulated DRAM latency is not constant, in contrast to [1]’s claim. Moreover, the Mess paper’s artifact repository [4, 5] lacks the necessary sources (simulator code, system configurations, memory traces, etc.) to fully reproduce all the Mess paper’s results. We find that the experiment scripts in [4, 5] use simulator executables and other resources that are neither described in the Mess paper nor found in the artifact repository [4, 5].Our work identifies important issues in [1]’s memory simulator evaluation methodology regarding Ramulator 2.0 and DAMOV. We present results that validate the real-system modeling accuracy of Ramulator 2.0, and describe the reasons why [1]’s results with respect to these two simulators are incorrect. We emphasize the importance of carefully and rigorously validating simulation results to avoid publishing factually incorrect results and contributions. We strongly encourage the computer architecture community to consider our corrections to the Ramulator 2.0 and DAMOV results of the Mess paper [1] to prevent the propagation of inaccurate and misleading results and to maintain the reliability of the scientific record. Our investigation also aims to stimulate discussion on artifact evaluation practices and on mechanisms for correcting results and artifacts after publication. To aid future works and reproduction of all our results, we open source all our code and scripts. We also discuss best practices and add sanity checks to aid users and developers of simulation tools.

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.

Modern DRAM chips are subject to read disturbance errors. These errors manifest as security-critical bitflips in a victim DRAM row that is physically nearby a repeatedly activated (opened) aggressor row (RowHammer) or an aggressor row that is kept open for a long time (RowPress). State-of-the-art read disturbance mitigations rely on accurate and exhaustive characterization of the read disturbance threshold ($R D T$) (e.g., the number of aggressor row activations needed to induce the first RowHammer or RowPress bitflip) of every DRAM row (of which there are millions or billions in a modern system) to prevent read disturbance bitflips securely and with low overhead. We experimentally demonstrate for the first time that the RDT of a DRAM row significantly and unpredictably changes over time. We call this new phenomenon variable read disturbance (VRD). Our extensive experiments using 160 DDR4 chips and 4 HBM2 chips from three major manufacturers yield three key observations. First, it is very unlikely that relatively few RDT measurements can accurately identify the RDT of a DRAM row. The minimum RDT of a DRAM row appears after tens of thousands of measurements (e.g., up to 94,467), and the minimum RDT of a DRAM row is $3.5 \times$ smaller than the maximum RDT observed for that row. Second, the probability of accurately identifying a row’s RDT with a relatively small number of measurements reduces with increasing chip density or smaller technology node size. Third, data pattern, the amount of time an aggressor row is kept open, and temperature can affect the probability of accurately identifying a DRAM row’s RDT. Our empirical results have implications for the security guarantees of read disturbance mitigation techniques: if the RDT of a DRAM row is not identified accurately, these techniques can easily become insecure. We discuss and evaluate using a guardband for RDT and error-correcting codes for mitigating read disturbance bitflips in the presence of RDTs that change unpredictably over time. We conclude that $a\gt 10 \%$ guardband for the minimum observed RDT combined with SECDED or Chipkill-like SSC error-correcting codes could prevent read disturbance bitflips at the cost of large read disturbance mitigation performance overheads (e.g., 45% performance loss for an RDT guardband of $50 \%$). We hope and believe future work on efficient online profiling mechanisms and configurable read disturbance mitigation techniques could remedy the challenges imposed on today’s read disturbance mitigations by the variable read disturbance phenomenon.

Processing-using-DRAM (PuD) is a promising paradigm for alleviating the data movement bottleneck using a DRAM array's massive internal parallelism and bandwidth to execute very wide dataparallel operations.Performing a PuD operation involves activating multiple DRAM rows in quick succession or simultaneously, i.e., multiple-row activation.Multiple-row activation is fundamentally different from conventional memory access patterns that activate one DRAM row at a time.However, repeatedly activating even one DRAM row (e.g., RowHammer) can induce bitflips in unaccessed DRAM rows because modern DRAM is subject to read disturbance, a worsening safety, security, and reliability issue.Unfortunately, no prior work investigates the effects of multiple-row activation, as commonly used by PuD operations, on DRAM read disturbance.In this paper, we present the first characterization study of read disturbance effects of multiple-row activation-based PuD (which we call PuDHammer) using 316 real DDR4 DRAM chips from four major DRAM manufacturers.Our detailed characterization results covering various operational conditions and parameters (i.e., temperature, data patterns, access patterns, timing parameters, and spatial variation) show that 1) PuDHammer significantly exacerbates the read disturbance vulnerability, causing up to 158.58× reduction in the minimum hammer count required to induce the first bitflip (𝐻𝐶 𝑓 𝑖𝑟𝑠𝑡 ), compared to RowHammer, 2) PuDHammer is affected by various operational conditions and parameters, 3) combining RowHammer with PuDHammer is more effective than using

We experimentally demonstrate a new widespread read disturbance phenomenon, ColumnDisturb, in real commodity DRAM chips.By repeatedly opening or keeping a DRAM row (aggressor row) open, we show that it is possible to disturb DRAM cells through a DRAM column (i.e., bitline) and induce bitflips in DRAM cells sharing the same columns as the aggressor row (across multiple DRAM subarrays).With ColumnDisturb, the activation of a single row concurrently disturbs DRAM cells across as many as three DRAM subarrays (e.g., up to 3072 DRAM rows in tested DDR4 DRAM chips) as opposed to RowHammer & RowPress, which affect only a few neighboring rows of the aggressor row in a single subarray.We rigorously and comprehensively characterize ColumnDisturb and its characteristics under various operational conditions (i.e., temperature, data pattern, DRAM timing parameters, average voltage level of the bitline, memory access pattern, and spatial variation) using 216 DDR4 and 4 HBM2 chips from three major DRAM manufacturers.Among our 27 key experimental observations, we highlight two major results and their implications.First, ColumnDisturb affects chips from all three major DRAM manufacturers and worsens as DRAM technology scales down to smaller node sizes (e.g., the minimum time to induce the first Col-umnDisturb bitflip reduces by up to 5.06x and 2.96x on average across all tested modules).We observe that, even in existing DRAM chips, ColumnDisturb induces bitflips within a nominal DDR4 refresh window (e.g., in 63.6 ms) in multiple cells from one module.We predict that, as DRAM technology node size reduces, ColumnDisturb would worsen in future DRAM chips, likely causing many more bitflips in the nominal refresh window.Second, beyond the nominal refresh window, ColumnDisturb induces bitflips in many (up to 198x) more DRAM rows than retention failures.Therefore, Column-Disturb has strong implications for existing retention-aware refresh mechanisms that aim to improve system performance and energy efficiency by leveraging the heterogeneity in DRAM cell retention

Modern DRAM is vulnerable to read disturbance (e.g., RowHammer and RowPress) that significantly undermines the robust operation of the system. Repeatedly opening and closing a DRAM row (RowHammer) or keeping a DRAM row open for a long period of time (RowPress) induces bitflips in nearby unaccessed DRAM rows. Prior works on DRAM read disturbance either 1) perform experimental characterization using commercial-off-the-shelf (COTS) DRAM chips to demonstrate the high-level characteristics of the read disturbance bitflips, or 2) perform device-level simulations to understand the low-level error mechanisms of the read disturbance bitflips.In this paper, we attempt to align and cross-validate the real-chip experimental characterization results and state-of-the-art device-level studies of DRAM read disturbance. To do so, we first identify and extract the key bitflip characteristics of RowHammer and RowPress from the device-level error mechanisms studied in prior works. Then, we perform experimental characterization on 96 COTS DDR4 DRAM chips that directly match the data and access patterns studied in the device-level works. Through our experiments, we identify fundamental inconsistencies in the RowHammer and RowPress bitflip directions and access pattern dependence between experimental characterization results and the device-level error mechanisms.Based on our results, we hypothesize that either 1) the retention failure based DRAM architecture reverse-engineering methodologies do not fully work on modern DDR4 DRAM chips, or 2) existing device-level works do not fully uncover all the major read disturbance error mechanisms. We hope our findings inspire and enable future works to build a more fundamental and comprehensive understanding of DRAM read disturbance.

Modern Machine Learning (ML) training on large-scale datasets is a very time-consuming workload. It relies on the optimization algorithm Stochastic Gradient Descent (SGD) due to its effectiveness, simplicity, and generalization performance (i.e., test performance on unseen data). Processor-centric architectures (e.g., CPUs, GPUs) commonly used for modern ML training workloads based on SGD are bottlenecked by data movement between the processor and memory units due to the poor data locality in accessing large training datasets. As a result, processor-centric architectures suffer from low performance and high energy consumption while executing ML training workloads. Processing-In-Memory (PIM) is a promising solution to alleviate the data movement bottleneck by placing the computation mechanisms inside or near memory. Several prior works propose PIM techniques to accelerate ML training; however, prior works either do not consider real-world PIM systems or evaluate algorithms that are not widely used in modern ML training.

We experimentally demonstrate the effects of read disturbance (RowHammer and RowPress) and uncover the inner workings of undocumented read disturbance defense mechanisms in High Bandwidth Memory (HBM). Detailed characterization of six real HBM2 DRAM chips in two different FPGA boards shows that (1) the read disturbance vulnerability significantly varies between different HBM2 chips and between different components (e.g., 3D-stacked channels) in-side a chip, (2) DRAM rows at the end and in the middle of a bank are more resilient to read disturbance, (3) fewer additional activations are sufficient to induce more read disturbance bitflips in a DRAM row if the row exhibits the first bitflip at a relatively high activation count, (4) a modern HBM2 chip implements undocumented read disturbance defenses that track potential aggressor rows based on how many times they are activated. We describe how our findings could be leveraged to develop more powerful read disturbance attacks and more efficient defense mechanisms. We open source all our code and data to facilitate future research at https://github.com/CMU-SAFARI/HBM-Read-Disturbance.

We experimentally analyze the computational capability of commercial off-the-shelf(COTS) DRAM chips and the robustness of these capabilities under various timing delays between DRAM commands, data patterns, temperature, and voltage levels. We extensively characterize 120 COTS DDR4 chips from two major manufacturers. We highlight four key results of our study. First, COTS DRAM chips are capable of 1) simultaneously activating up to 32 rows (i.e., simultaneous many-row activation), 2) executing a majority of X (MAJX) operation where X>3 (i.e., MAJ5, MAJ7, and MAJ9 operations), and 3) copying a DRAM row (concurrently) to up to 31 other DRAM rows, which we call Multi-RowCopy. Second, storing multiple copies of MAJX's input operands on all simultaneously activated rows drastically increases the success rate (i.e., the percentage of DRAM cells that correctly perform the computation) of the MAJX operation. For example, MAJ3 with 32-row activation (i.e., replicating each MAJ3's input operands 10 times) has a 30.81% higher average success rate than MAJ3 with 4-row activation (i.e., no replication). Third, data pattern affects the success rate of MAJX and MUlti-RowCopy operations by 11.52% and 0.07% on average. Fourth, simultaneous many-row activation, MAJX, and Multi-RowCopy operations are highly resilient to temperature and voltage changes, with small success rate variations of at most 2.13% among all tested operations. We believe these empirical results demonstrate the promising potential of using DRAM as a computation substrate. To aid future research and development, we open-source our infrastructure at https://github.com/CMU-SAFARI/SiMRA-DRAM.

Read disturbance in modern DRAM chips is a widespread phenomenon and is reliably used for breaking memory isolation, a fundamental building block for building robust systems. RowHammer and RowPress are two examples of read disturbance in DRAM where repeatedly accessing (hammering) or keeping active (pressing) a memory location induces bitflips in other memory locations. Unfortunately, shrinking technology node size exacerbates read disturbance in DRAM chips over generations. As a result, existing defense mechanisms suffer from significant performance and energy overheads, limited effectiveness, or prohibitively high hardware complexity. In this paper, we tackle these shortcomings by leveraging the spatial variation in read disturbance across different memory locations in real DRAM chips. To do so, we 1) present the first rigorous real DRAM chip characterization study of spatial variation of read disturbance and 2) propose Svärd, a new mechanism that dynamically adapts the aggressiveness of existing solutions based on the row-level read disturbance profile. Our experimental characterization on 144 real DDR4 DRAM chips representing 11 die revisions demonstrates a large variation in read disturbance vulnerability across different memory locations: in the part of memory with the worst read disturbance vulnerability, 1) up to 2 × the number of bitflips can occur and 2) bitflips can occur at an order of magnitude fewer accesses, compared to the memory locations with the least vulnerability to read disturbance. Svärd leverages this variation to reduce the overheads of five state-of-the-art read disturbance solutions, and thus significantly increases system performance.

Processing-using-DRAM (PuD) is an emerging paradigm that leverages the analog operational properties of DRAM circuitry to enable massively parallel in-DRAM computation. PuD has the potential to significantly reduce or eliminate costly data movement between processing elements and main memory. A common approach for PuD architectures is to make use of bulk bitwise computation (e.g., AND, OR, NOT). Prior works experimentally demonstrate three-input MAJ (i.e., MAJ3) and two-input AND and OR operations in commercial off-the-shelf (COTS) DRAM chips. Yet, demonstrations on COTS DRAM chips do not provide a functionally complete set of operations (e.g., NAND or AND and NOT). We experimentally df performing 1) functionally-complete Boolean operations: NOT, NAND, and NOR and 2) many-input (i.e., more than two-input) AND and OR operations. We present an extensive characterization of new bulk bitwise operations in 256 off-theshelf modern DDR4 DRAM chips. We evaluate the reliability of these operations using a metric called success rate: the fraction of correctly performed bitwise operations. Among our 19 new observations, we highlight four major results. First, we can perform the NOT operation on COTS DRAM chips with a 98.37% success rate on average. Second, we can perform up to 16-input NAND, NOR, AND, and OR operations on COTS DRAM chips with high reliability (e.g., 16-input NAND, NOR, AND, and OR with an average success rate of 94.94%, 95.87%, 94.94%, and 95.85%, respectively). Third, data pattern only slightly affects NAND, NOR, AND, and OR operations. Our results show that executing NAND, NOR, AND, and OR operations with random data patterns decreases the success rate compared to all logic-1/logic-0 patterns by 1.39%, 1.97%, 1.43%, and 1.98%, respectively. Fourth, NOT, NAND, NOR, AND, and OR operations are highly resilient to temperature changes, with small success rate fluctuations of at most 1.66% among all the tested operations when the temperature is increased from 50°C to 95°C. We believe these empirical results demonstrate the promising potential of using DRAM as a computation substrate.

The memory controller is in charge of managing DRAM maintenance operations (e.g., refresh, RowHammer protection, memory scrubbing) to reliably operate modern DRAM chips. Implementing new maintenance operations often necessitates modifications in the DRAM interface, memory controller, and potentially other system components. Such modifications are only possible with a new DRAM standard, which takes a long time to develop, likely leading to slow progress in the adoption of new architectural techniques in DRAM chips. We propose a new low-cost DRAM architecture, Self-Managing DRAM (SMD), that enables autonomous in-DRAM maintenance operations by transferring the responsibility for controlling main-tenance operations from the memory controller to the SMD chip. To enable autonomous maintenance operations, we make a single, simple modification to the DRAM interface, such that an SMD chip rejects memory controller accesses to DRAM regions (e.g., a subarray or a bank) under maintenance, while allowing memory accesses to other DRAM regions. Thus, SMD enables 1) implementing new in-DRAM maintenance mechanisms (or modifying existing ones) with no further changes in the DRAM interface, memory controller, or other system components, and 2) overlapping the latency of a maintenance operation in one DRAM region with the latency of accessing data in another. We evaluate SMD and show that it 1) can be implemented without adding new pins to the DDRx interface with low latency (0.4% of row activation latency) and area (1.1% of a 45.5 mm2 DRAM chip) overhead, 2) achieves 4.1 % average speedup across 20 four-core memory-intensive workloads over a DDR4-based system/-DRAM co-design technique that intelligently parallelizes main-tenance operations with memory accesses, and 3) guarantees for-ward progress for rejected memory accesses. We believe and hope SMD can enable innovations in DRAM architecture to rapidly come to fruition. We open source all SMD source code and data at https://github.com/CMU-SAFARI/SelfManagingDRAM.

Memory isolation is critical for system reliability, security, and safety. Unfortunately, read disturbance can break memory isolation in modern DRAM chips. For example, RowHammer is awell-studied read-disturb phenomenon where repeatedly opening and closing (i.e., hammering) a DRAM row many times causes bitflips in physically nearby rows.

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.

RowHammer is a circuit-level DRAM vulnerability, where repeatedly activating and precharging a DRAM row, and thus alternating the voltage of a row’s wordline between low and high voltage levels, can cause bit flips in physically nearby rows. Recent DRAM chips are more vulnerable to RowHammer: with technology node scaling, the minimum number of activate-precharge cycles to induce a RowHammer bit flip reduces and the RowHammer bit error rate increases. Therefore, it is critical to develop effective and scalable approaches to protect modern DRAM systems against RowHammer. To enable such solutions, it is essential to develop a deeper understanding of the RowHammer vulnerability of modern DRAM chips. However, even though the voltage toggling on a wordline is a key determinant of RowHammer vulnerability, no prior work experimentally demonstrates the effect of wordline voltage (VPP) on the RowHammer vulnerability. Our work closes this gap in understanding.This is the first work to experimentally demonstrate on 272 real DRAM chips that lowering VPPreduces a DRAM chip’s RowHammer vulnerability. We show that lowering VPP1) increases the number of activate-precharge cycles needed to induce a RowHammer bit flip by up to 85.8 % with an average of 7.4 % across all tested chips and 2) decreases the RowHammer bit error rate by up to 66.9 % with an average of 15.2 % across all tested chips. At the same time, reducing VPPmarginally worsens a DRAM cell’s access latency, charge restoration, and data retention time within the guardbands of system-level nominal timing parameters for 208 out of 272 tested chips. We conclude that reducing VPPis a promising strategy for reducing a DRAM chip’s RowHammer vulnerability without requiring modifications to DRAM chips.

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.

DRAM is the dominant main memory technology used in modern computing systems. Computing systems implement a memory controller that interfaces with DRAM via DRAM commands. DRAM executes the given commands using internal components (e.g., access transistors, sense amplifiers) that are orchestrated by DRAM internal timings, which are fixed for each DRAM command. Unfortunately, the use of fixed internal timings limits the types of operations that DRAM can perform and hinders the implementation of new functionalities and custom mechanisms that improve DRAM reliability, performance and energy. To overcome these limitations, we propose enabling programmable DRAM internal timings for controlling in-DRAM components.To this end, we design CODIC, a new low-cost DRAM substrate that enables fine-grained control over four previously fixed internal DRAM timings that are key to many DRAM operations. We implement CODIC with only minimal changes to the DRAM chip and the DDRx interface. To demonstrate the potential of CODIC, we propose two new CODIC-based security mechanisms that outperform state-of-the-art mechanisms in several ways: (1) a new DRAM Physical Unclonable Function (PUF) that is more robust and has significantly higher throughput than state-of-the-art DRAM PUFs, and (2) the first cold boot attack prevention mechanism that does not introduce any performance or energy overheads at runtime.

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.

RowHammer is a circuit-level DRAM vulnerability where repeatedly accessing (i.e., hammering) a DRAM row can cause bit flips in physically nearby rows. The RowHammer vulnerability worsens as DRAM cell size and cell-to-cell spacing shrink. Recent studies demonstrate that modern DRAM chips, including chips previously marketed as RowHammer-safe, are even more vulnerable to RowHammer than older chips such that the required hammer count to cause a bit flip has reduced by more than 10X in the last decade. Therefore, it is essential to develop a better understanding and in-depth insights into the RowHammer vulnerability of modern DRAM chips to more effectively secure current and future systems.

DRAM is the prevalent main memory technology, but its long access latency can limit the performance of many workloads. Although prior works provide DRAM designs that reduce DRAM access latency, their reduced storage capacities hinder the performance of workloads that need large memory capacity. Because the capacity-latency trade-off is fixed at design time, previous works cannot achieve maximum performance under very different and dynamic workload demands.This paper proposes Capacity-Latency-Reconfigurable DRAM (CLR-DRAM), a new DRAM architecture that enables dynamic capacity-latency trade-off at low cost. CLR-DRAM allows dynamic reconfiguration of any DRAM row to switch between two operating modes: 1) max-capacity mode, where every DRAM cell operates individually to achieve approximately the same storage density as a density-optimized commodity DRAM chip and 2) high-performance mode, where two adjacent DRAM cells in a DRAM row and their sense amplifiers are coupled to operate as a single low-latency logical cell driven by a single logical sense amplifier.We implement CLR-DRAM by adding isolation transistors in each DRAM subarray. Our evaluations show that CLR-DRAM can improve system performance and DRAM energy consumption by 18.6% and 29.7% on average with four-core multiprogrammed workloads. We believe that CLR-DRAM opens new research directions for a system to adapt to the diverse and dynamically changing memory capacity and access latency demands of workloads.