We propose a soft error tolerant flip-flop (FF) design to protect configuration storage cells in standard cell-based em-bedded FPGA fabrics used in SoC designs. Traditional rad-hard FFs such as DICE and Triple Modular Redundant (TMR) use additional redundant storage nodes for soft error tolerance and hence incur high area overheads. Since the eFPGA configuration storage is static, the master latch of the FF is transparent and unused, except when a configuration is loaded. The proposed dual-storage-mode (DSM) FF reuses the master and slave latches as redundant storage along with a C-element for error correction. The DSM FF was fabricated on a 22nm FinFET process along with standard D-FF, pulse DICE FF, and TMR FF designs to evaluate soft error tolerance. The radiation test results show that the DSM FF can reduce the error cross section by more than three orders of magnitude (3735X) compared to the standard D-FF and two orders of magnitude (455X) compared to the pulse DICE FF with a comparable area. Furthermore, the DSM FF is 42% smaller than the TMR FF with a similar error cross section.

Growing IC manufacturing complexity and reliance on third-party fabrication create supply chain fragility, contributing to chip shortages and IP security risks. General-purpose ICs can mitigate manufacturing security risks but rely on software-based configurations, which is not optimal for high-consequence applications. Our work proposes a novel IP-agnostic Foundational Cell Array (FC-Array) platform to overcome these challenges. Built on verified standard cells and industry-standard EDA tools, this platform preserves many advantages of an ASIC. By incorporating 3D split manufacturing, we provide semantically secure IP protection and a base wafer that can be stockpiled. Our tests demonstrate both power-efficient (100 MHz) and high-performance (1 GHz) options. In a post-place-and-route simulated 28nm design, our FC-Array shows a worst-case 1.85x increase in power consumption and a 2.61x increase in area compared to standard cell ASICs for equivalent timing performance.

•Dedicated FFT hardware is typically designed as a standalone block and then integrated into systems •Relatively fixed functionality once implemented •System-level integration with FFT-based application software becomes manual and challenging to optimize

Whether powered by a battery or energy harvested from the environment, low-power (LP) sensor devices require extreme energy efficiency. These sorts of devices are becoming pervasive, running increasingly sophisticated applications in inhospitable environments. We present Manic, an energy-efficient microcontroller (MCU) augmented with a vector-dataflow (VDF) co-processor. The testchip taped out on a 22nm bulk finFET CMOS process demonstrates that Manic is 60% more energy-efficient than a baseline, scalar, low-power MCU, achieving peak efficiency of 256 MOPS/mW (2.6× prior work) while consuming only$19.1 \mu\mathrm{W}$(@4MHz). To make the system viable for intermittently powered applications that require non-volatile storage, Manic includes a 256KB embedded MRAM.

Learning-based visual odometry (VO) has gained increasing popularity in autonomous navigation of small robots. However, most methods in the category require computation resources not normally available on edge systems. We contend that specialized hardware accelerators are ideal solutions to this problem because of their superior energy efficiency. In this paper, we first propose a model to derive compute specifications for VO from physical characteristics of unmanned aerial vehicles (UAVs). These specifications serve as the basis to guide our accelerator design process. Based on the specifications derived from the DJI Mavic Air 2 and Crazyflie 2.0 UAVs, we explore the speed/flight-time design spaces for three target VO algorithms on two NVIDIA Jetson systems. Then, we propose a hardware accelerator architecture and present prototype implementations based on FPGAs. Additionally, we illustrate the algorithm/hardware co-design approach with a series of hardware-aware algorithmic redesigns targeting the FPGA prototypes, and quantify the throughput-accuracy tradeoff of them. Our FPGA implementation of DFVO is 2.7x more energy efficient compared to off-the-shelf embedded computers.

In recent years, IC reverse engineering and IC fabrication supply chain security have grown to become significant economic and security threats for designers, system integrators, and end customers. Many of the existing logic locking and obfuscation techniques have shown to be vulnerable to attack once the attacker has access to the design netlist either through reverse engineering or through an untrusted fabrication facility. We introduce soft embedded FPGA redaction, a hardware obfuscation approach that allows the designer substitute security-critical IP blocks within a design with a synthesizable eFPGA fabric. This method fully conceals the logic and the routing of the critical IP and is compatible with standard ASIC flows for easy integration and process portability. To demonstrate eFPGA redaction, we obfuscate a RISC-V control path and a GPS P-code generator. We also show that the modified netlists are resilient to SAT attacks with moderate VLSI overheads. The secure RISC-V design has 1.89x area and 2.36x delay overhead while the GPS design has 1.39x area and negligible delay overhead when implemented on an industrial 22nm FinFET CMOS process.

In recent years, IC reverse engineering and IC fabrication supply chain security have grown to become significant economic and security threats for designers, system integrators, and end customers. Many of the existing logic locking and obfuscation techniques have shown to be vulnerable to attack once the attacker has access to the design netlist either through reverse engineering or through an untrusted fabrication facility. We introduce soft embedded FPGA redaction, a hardware obfuscation approach that allows the designer substitute security-critical IP blocks within a design with a synthesizable eFPGA fabric. This method fully conceals the logic and the routing of the critical IP and is compatible with standard ASIC flows for easy integration and process portability. To demonstrate eFPGA redaction, we obfuscate a RISC-V control path and a GPS P-code generator. We also show that the modified netlists are resilient to SAT attacks with moderate VLSI overheads. The secure RISC-V design has 1.89x area and 2.36x delay overhead while the GPS design has 1.39x area and negligible delay overhead when implemented on an industrial 22nm FinFET CMOS process.

Ultra-low-power (ULP) devices are becoming pervasive, enabling many emerging sensing applications. Energy-efficiency is paramount in these applications, as efficiency determines device lifetime in battery-powered deployments and performance in energy-harvesting deployments. Unfortunately, existing designs fall short because ASICs’ upfront costs are too high and prior ULP architectures are too inefficient or inflexible.We present Snafu, the first framework to flexibly generate ULP coarse-grain reconfigurable arrays (CGRAs). Snafu provides a standard interface for processing elements (PE), making it easy to integrate new types of PEs for new applications. Unlike prior high-performance, high-power CGRAs, Snafu is designed from the ground up to minimize energy consumption while maximizing flexibility. Snafu saves energy by configuring PEs and routers for a single operation to minimize switching activity; by minimizing buffering within the fabric; by implementing a statically routed, bufferless, multi-hop network; and by executing operations in-order to avoid expensive tag-token matching.We further present Snafu-Arch, a complete ULP system that integrates an instantiation of the Snafu fabric alongside a scalar RISC-V core and memory. We implement Snafu in RTL and evaluate it on an industrial sub-28 nm FinFET process across a suite of common sensing benchmarks. Snafu-Arch operates at <1 mW, orders-of-magnitude less power than most prior CGRAs. Snafu-Arch uses 41% less energy and runs 4.4× faster than the prior state-of-the-art general-purpose ULP architecture. Moreover, we conduct three comprehensive case-studies to quantify the cost of programmability in Snafu. We find that Snafu-Arch is close to ASIC designs built in the same technology, using just 2.6× more energy on average.

Design methodologies for synthesizing FPGA fabrics presented in the literature typically employ a bottom-up approach wherein individual tiles are synthesized in isolation and later stitched together to generate the large FPGA fabric. However, using a bottom-up methodology to ensure fabric-level performance targets is challenging due to the lack of a global timing view across multiple tiles spanning the FPGA fabric. While previous works address this problem with a combination of manual buffering and floorplanning, these additional steps introduce significant deviations from standard push-button ASIC flows. In this paper, a top-down synthesis methodology is proposed, which eliminates the need for floorplanning and manual buffering by providing a global timing view of the FPGA fabric. To evaluate the proposed design methodology, we developed an FPGA fabric generator using the Chisel hardware construction language. The fabric generator reads in the Verilog-to-Routing architecture file, describing the user-defined FPGA fabric, and generates the Verilog netlist and timing exceptions required to automatically place and route the FPGA fabric in any technology node with a standard cell library. Post layout timing analysis of placed and routed FPGA fabrics on a 28nm industrial CMOS process demonstrates that the top-down methodology can place and route fabrics without the need for any manual buffering or floorplanning while providing ~20% average improvement in performance across multiple benchmark designs.

This paper presents the first 28 nm ASIC implementation of an accelerator for the post-quantum digital signature scheme XMSS. In particular, this paper presents an architecture for a novel, pipelined XMSS Leaf accelerator for accelerating the most compute-intensive step in the XMSS algorithm. This paper then presents the ASIC designs for both an existing non-pipelined accelerator architecture and the novel, pipelined XMSS Leaf accelerator. In addition, the performance of the 28 nm ASIC is compared to the same designs on 28 nm Artix-7 FPGA. The novel pipelined XMSS Leaf accelerator is 25% faster compared to the non-pipelined version in the ASIC, and both accelerator architectures have a 10 × lower power consumption than on the FPGA. The evaluation shows that the pipelining increases the frequency by 1.7× on the FPGA but only 1.2× on the ASIC, due to the critical path in the ASIC being in the memory. The non-pipelined XMSS Leaf accelerator is shown to have a significantly better area-delay and energy-delay metric on the ASIC, while the pipelined accelerator wins out in these metrics on the FPGA. Consequently, this work shows the different architectural decisions that need to be made between FPGA and ASIC designs, when selecting how to best implement post-quantum cryptographic accelerators in hardware.

Modern NAND flash memory chips provide high density by storing two bits of data in each flash cell, called a multi-level cell (MLC). An MLC partitions the threshold voltage range of a flash cell into four voltage states. When a flash cell is programmed, a high voltage is applied to the cell. Due to parasitic capacitance coupling between flash cells that are physically close to each other, flash cell programming can lead to cell-to-cell program interference, which introduces errors into neighboring flash cells. In order to reduce the impact of cell-to-cell interference on the reliability of MLC NAND flash memory, flash manufacturers adopt a two-step programming method, which programs the MLC in two separate steps. First, the flash memory partially programs the least significant bit of the MLC to some intermediate threshold voltage. Second, it programs the most significant bit to bring the MLC up to its full voltage state. In this paper, we demonstrate that two-step programming exposes new reliability and security vulnerabilities. We experimentally characterize the effects of two-step programming using contemporary 1X-nm (i.e., 15–19nm) flash memory chips. We find that a partially-programmed flash cell (i.e., a cell where the second programming step has not yet been performed) is much more vulnerable to cell-to-cell interference and read disturb than a fully-programmed cell. We show that it is possible to exploit these vulnerabilities on solid-state drives (SSDs) to alter the partially-programmed data, causing (potentially malicious) data corruption. Building on our experimental observations, we propose several new mechanisms for MLC NAND flash memory that eliminate or mitigate data corruption in partially-programmed cells, thereby removing or reducing the extent of the vulnerabilities, and at the same time increasing flash memory lifetime by 16%.

Retention errors, caused by charge leakage over time, are the dominant source of flash memory errors. Understanding, characterizing, and reducing retention errors can significantly improve NAND flash memory reliability and endurance. In this paper, we first characterize, with real 2y-nm MLC NAND flash chips, how the threshold voltage distribution of flash memory changes with different retention age - the length of time since a flash cell was programmed. We observe from our characterization results that 1) the optimal read reference voltage of a flash cell, using which the data can be read with the lowest raw bit error rate (RBER), systematically changes with its retention age, and 2) different regions of flash memory can have different retention ages, and hence different optimal read reference voltages. Based on our findings, we propose two new techniques. First, Retention Optimized Reading (ROR) adaptively learns and applies the optimal read reference voltage for each flash memory block online. The key idea of ROR is to periodically learn a tight upper bound, and from there approach the optimal read reference voltage. Our evaluations show that ROR can extend flash memory lifetime by 64% and reduce average error correction latency by 10.1%, with only 768 KB storage overhead in flash memory for a 512 GB flash-based SSD. Second, Retention Failure Recovery (RFR) recovers data with uncorrectable errors offline by identifying and probabilistically correcting flash cells with retention errors. Our evaluation shows that RFR reduces RBER by 50%, which essentially doubles the error correction capability, and thus can effectively recover data from otherwise uncorrectable flash errors.

Despite best efforts, integrated systems are “born” (manufactured) with a unique `personality' that stems from our inability to precisely fabricate their underlying circuits, and create software a priori for controlling the resulting uncertainty. It is possible to use sophisticated test methods to identify the best-performing systems but this would result in unacceptable yields and correspondingly high costs. The system personality is further shaped by its environment (e.g., temperature, noise and supply voltage) and usage (i.e., the frequency and type of applications executed), and since both can fluctuate over time, so can the system's personality. Systems also “grow old” and degrade due to various wear-out mechanisms (e.g., negative-bias temperature instability), and unexpectedly due to various early-life failure sources. These “nature and nurture” influences make it extremely difficult to design a system that will operate optimally for all possible personalities. To address this challenge, we propose to develop statistical learning in-chip (SLIC). SLIC is a holistic approach to integrated system design based on continuously learning key personality traits on-line, for self-evolving a system to a state that optimizes performance hierarchically across the circuit, platform, and application levels. SLIC will not only optimize integrated-system performance but also reduce costs through yield enhancement since systems that would have before been deemed to have weak personalities (unreliable, faulty, etc.) can now be recovered through the use of SLIC.

As process technologies have scaled, the increasing number of processor cores and memories on a single die has also driven the need for more complex on-chip interconnection networks. Crossbar switches are primary building blocks in such networks-on-chip, as they can be used as fast single-stage networks or as the core of the router switch in multi-stage networks. While crossbars offer non-blocking, single-hop, all-to-all communication, they tend to scale poorly with the number of nodes due to the latency and energy of the long wires and high-radix multiplexor structures needed. To combat these limitations, we propose a low-swing crossbar design that uses capacitively driven wires and capacitively coupled multiplexers. Capacitively driven wires offer low swing signaling, higher bandwidths, and low energy consumption, while capacitively coupled multiplexers offer reduced parasitic loading from the inactive inputs. We present a 16×16 64b low-swing crossbar switch designed in a TSMC 40nm CMOS bulk process. Post-layout simulation shows it operating at a maximum frequency of 2.2GHz, achieving a bandwidth of 2.56Tb/s at 0.9V (nominal Vdd) with an area of 0.94mm2. Total energy consumption for full, half, and minimum bandwidths are 110pJ, 84pJ, and 64pJ respectively, thus offering an efficiency of 10.49 Tbps/W, a 3X improvement over previously published results.

Computer architects are increasingly interested in evaluating their ideas at the register-transfer level (RTL) to gain more precise insights on the key characteristics (frequency, area, power) of a micro/architectural design proposal. However, the RTL synthesis process is notoriously tedious, slow, and errorprone and is often outside the area of expertise of a typical computer architect, as it requires familiarity with complex CAD flows, hard-to-get tools and standard cell libraries. The effort is further multiplied when targeting multiple technology nodes and standard cell variants to study technology dependence. This paper presents DELPHI, a flexible, open framework that leverages the DSENT modeling engine for faster, easier, and more efficient characterization of RTL hardware designs. DELPHI first synthesizes a Verilog or VHDL RTL design (either using the industry-standard Synopsys Design Compiler tool or a combination of open-source tools) to an intermediate structural netlist. It then processes the resulting synthesized netlist to generate a technology-independent DSENT design model. This model can then be used within a modified version of the DSENT flow to perform very fast—one to two orders of magnitude faster than full RTL synthesis—estimation of hardware performance characteristics, such as frequency, area, and power across a variety of DSENT technology models (e.g., 65nm Bulk, 32nm SOI, 11nm Tri-Gate, etc.). In our evaluation using 26 RTL design examples, DELPHI and DSENT were consistently able to closely track and capture design trends of conventional RTL synthesis results without the associated delay and complexity. We are releasing the full DELPHI framework (including a fully open-source flow) at http://www.ece.cmu.edu/CALCM/delphi/.

The crossbar is a popular topology for on-chip networks that offers non-blocking connectivity and uniform latency. However, as the number of nodes increases, crossbars typically scale poorly in area, power, and latency/throughput. To better understand the design space, we have developed an on-chip crossbar modeling tool based on analytical models calibrated using circuit-level simulation results in 40nm CMOS. We present a design space exploration showing how crossbar area, power, and performance vary across input/output node number, data width, wire parameters, and circuit implementation. Using the modeling results, we identify a design point that demonstrates 2X higher throughput, 1.4X lower power and 1.2X lower area compared to previous published designs.

Creating backdoors in integrated circuits (ICs), stealing hardware intellectual property, counterfeiting electronic components, reverse engineering ICs, and injecting malware in ICs are no longer nation state acts requiring specialized, expensive, and unlimited resources. Democratization of IC design has created numerous opportunities for rogues throughout the IC supply chain to inflict these attacks with aplomb and for a variety of reasons: personal gain, economic harm, economic gain, bringing disrepute, and sheer fun among others.

Physical unclonable functions (PUFs) are primitives that generate high-entropy, tamper resistant bits for use in secure systems. For applications such as cryptographic key generation, the PUF response bits must be highly reliable, consistent across multiple evaluations under voltage and temperature variations. Conventionally, error correcting codes (ECC) have been used to improve response reliability, but these techniques have significant area, power, and delay overheads and are vulnerable to information leakage. In this work, we present a highly-reliable, PUF-based, cryptographic key generator that uses no ECC, but instead uses built-in self-test to determine which PUF bits are reliable and only uses those bits for key generation. We implemented a prototype of the key generator in a 65nm bulk CMOS testchip. The key generator generates 1213 bits in an area of2with a measured bit error rate of*10-9in both the nominal and worst case corners (100k measurements each). This is equivalent to a 128-bit key failure rate of-6. The system can generate a 128-bit key in 1.15μs. Finally, we present a realization of a “strong”-PUF that uses 128 of these highly reliable bits in conjunction with an Advanced Encryption Standard (AES) cryptographic primitive and has a response time of 40ns and is realized in an area of 84k/μm2.

Continued scaling of NAND flash memory to smaller process technology nodes decreases its reliability, necessitating more sophisticated mechanisms to correctly read stored data values. To distinguish between different potential stored values, conventional techniques to read data from flash memory employ a single set of reference voltage values, which are determined based on the overall threshold voltage distribution of flash cells. Unfortunately, the phenomenon of program interference, in which a cell's threshold voltage unintentionally changes when a neighboring cell is programmed, makes this conventional approach increasingly inaccurate in determining the values of cells.

With continued scaling of NAND flash memory process technology and multiple bits programmed per cell, NAND flash reliability and endurance are degrading. Understanding, characterizing, and modeling the distribution of the threshold voltages across different cells in a modern multi-level cell (MLC) flash memory can enable the design of more effective and efficient error correction mechanisms to combat this degradation. We show the first published experimental measurement-based characterization of the threshold voltage distribution of flash memory. To accomplish this, we develop a testing infrastructure that uses the read retry feature present in some 2Y-nm (i.e., 20–24nm) flash chips. We devise a model of the threshold voltage distributions taking into account program/erase (P/E) cycle effects, analyze the noise in the distributions, and evaluate the accuracy of our model. A key result is that the threshold voltage distribution can be modeled, with more than 95% accuracy, as a Gaussian distribution with additive white noise, which shifts to the right and widens as P/E cycles increase. The novel characterization and models provided in this paper can enable the design of more effective error tolerance mechanisms for future flash memories.

As NAND flash memory continues to scale down to smaller process technology nodes, its reliability and endurance are degrading. One important source of reduced reliability is the phenomenon of program interference: when a flash cell is programmed to a value, the programming operation affects the threshold voltage of not only that cell, but also the other cells surrounding it. This interference potentially causes a surrounding cell to move to a logical state (i.e., a threshold voltage range) that is different from its original state, leading to an error when the cell is read. Understanding, characterizing, and modeling of program interference, i.e., how much the threshold voltage of a cell shifts when another cell is programmed, can enable the design of mechanisms that can effectively and efficiently predict and/or tolerate such errors. In this paper, we provide the first experimental characterization of and a realistic model for program interference in modern MLC NAND flash memory. To this end, we utilize the read-retry mechanism present in some state-of-the-art 2Y-nm (i.e., 20-24nm) flash chips to measure the changes in threshold voltage distributions of cells when a particular cell is programmed. Our results show that the amount of program interference received by a cell depends on 1) the location of the programmed cells, 2) the order in which cells are programmed, and 3) the data values of the cell that is being programmed as well as the cells surrounding it. Based on our experimental characterization, we develop a new model that predicts the amount of program interference as a function of threshold voltage values and changes in neighboring cells. We devise and evaluate one application of this model that adjusts the read reference voltage to the predicted threshold voltage distribution with the goal of minimizing erroneous reads. Our analysis shows that this new technique can reduce the raw flash bit error rate by 64% and thereby improve flash lifetime by 30%. We hope that the understanding and models developed in this paper lead to other error tolerance mechanisms for future flash memories.

Physically Unclonable Functions (PUFs) are structures with many applications, including device authentication, identification, and cryptographic key generation. In this paper we propose a new PUF, called SCAN-PUF, based on scan-chain power-up states. We argue that scan chains have multiple characteristics that make them uniquely suited as a low-cost PUF. We present results from test chips fabricated in a 65nm bulk CMOS process in support of these claims. While approximately 20% of the total population of scan elements are unreliable across temperature variations, we find that simple unanimous selection schemes can result in mean error rates of less than 0.1% for the selected populations across all measurements collected.

The embedded memory hierarchy of microprocessors and systems-on-a-chip plays a critical role in the overall system performance, area, power, resilience, and yield. However, as process technologies scale down to nanometer-regime geometries, the design and implementation of the embedded memory system are becoming increasingly difficult due to a number of exacerbating factors including increasing process variability, manufacturing defects, device wear out, and susceptibility to energetic particle strikes. Consequently, conventional memory resilience techniques will be unable to counter the raw bit error rate of the memory arrays in future technologies at economically feasible design points. Error correcting codes (ECC) are a widely-used and effective technique for correcting memory errors, but using conventional ECC techniques to correct more than one bit per word incurs high latency, area, and power overheads. In this work, we propose a novel ECC scheme based on erasure coding that can extend ECC to correct and detect multiple erroneous bits at low latency, area, and power overheads. Our results show that the increased memory resilience afforded by erasure-based ECC (EB-ECC) can be traded off to boost the memory performance, area, power, and yield. We show that EB-ECC, when combined with less than 5% row redundancy, can improve the cache access latency, power, and stability by over 40% on average, while maintaining near 100% yield and runtime reliability.

As NAND flash memory manufacturers scale down to smaller process technology nodes and store more bits per cell, reliability and endurance of flash memory reduce. Wear-leveling and error correction coding can improve both reliability and endurance, but finding effective algorithms requires a strong understanding of flash memory error patterns. To enable such understanding, we have designed and implemented a framework for fast and accurate characterization of flash memory throughout its lifetime. This paper examines the complex flash errors that occur at 30-40nm flash technologies. We demonstrate distinct error patterns, such as cycle-dependency, location-dependency and value-dependency, for various types of flash operations. We analyze the discovered error patterns and explain why they exist from a circuit and device standpoint. Our hope is that the understanding developed from this characterization serves as a building block for new error tolerance algorithms for flash memory.

The CoRAM memory architecture for FPGA-based computing augments traditional reconfigurable fabric with a natural and effective way for applications to interact with off-chip memory and I/O. The two central tenets of the CoRAM memory architecture are (1) the deliberate separation of concerns between computation versus data marshalling and (2) the use of a multithreaded software abstraction to replace FSM-based memory control logic. To evaluate the viability of the CoRAM memory architecture, we developed a full RTL implementation of a CoRAM microarchitecture instance that can be synthesized for standard cells or emulated on FPGAs. The results of our evaluation show that a soft emulation of the CoRAM memory architecture on current FPGAs can be impractical for memory-intensive, large-scale applications due to the high performance and area penalties incurred by the soft mechanisms. The results further show that in an envisioned FPGA built with CoRAM in mind, the introduction of hard macro blocks for data distribution can mitigate these inefficiencies---allowing applications to take advantage of the CoRAM memory architecture for ease of programmability and portability while still enjoying performance and efficiency comparable to RTL-level application development on conventional FPGAs.

With the continued scaling of NAND flash and multi-level cell technology, flash-based storage has gained widespread use in systems ranging from mobile platforms to enterprise servers. However, the robustness of NAND flash cells is an increasing concern, especially at nanometer-regime process geometries. NAND flash memory bit error rate increases exponentially with the number of program/erase cycles. Stronger error correcting codes (ECC) can be used to tolerate higher error rates, but these have diminishing returns with increasing P/E cycles and can have prohibitively high power, area, and latency overheads. The goal of this paper is to develop new techniques that can tolerate high bit error rates without requiring prohibitively strong ECC. Our techniques, called Flash Correct-and-Refresh (FCR) exploit the observation that the dominant error source in NAND flash memory is retention errors, caused by flash cells losing charge over time. The key idea is to periodically read, correct, and reprogram (in-place) or remap the stored data before it accumulates more retention errors than can be corrected by simple ECC. Detailed simulations of a solid-state drive (SSD) storage system driven by measured experimental data from error characterization on real flash memory chips show that our techniques provide 46× average lifetime improvement on a variety of workloads at no additional hardware cost. We also find that our techniques achieve lifetime improvements that cannot feasibly be achieved with stronger ECC.

NAND flash memory has been widely used for data storage due to its high density, high throughput, low cost, and low power. However, as flash memory manufacturers scale to smaller process technologies and store more bits per cell, the reliability and endurance of flash memory are decreasing. Wear-leveling and error correction coding can significantly improve both reliability and endurance, but finding effective algorithms requires quick and accurate characterization of flash memory error patterns. To this end, we have designed and implemented an FPGA-based open framework for quick, accurate, and comprehensive characterization of flash memories. Using this framework, we characterized detailed error patterns of flash memory throughout its entire lifetime. Our implementation uses an error accelerator block to decrease the test time by 20x. Based on these results, we propose and evaluate a smart bad block management policy in flash translation layer to increase SSD lifetime by up to 51%.

NAND Flash memory has been widely used for data storage due to its high density, high throughput, low cost, and low power. However, as the storage cells become smaller and with more bits programmed per cell, they are expected to suffer from reduced reliability and limited endurance. Wear-leveling and signal processing can significantly improve both reliability and endurance. However, finding optimal algorithms would require a quick and accurate characterization of Flash memory providing an insight into the error patterns. To this end, we have designed and implemented an FPGA-based framework for quick, accurate, and comprehensive characterization of Flash memories to allow efficient algorithm explorations.

FPGAs have been used in many applications to achieve orders-of-magnitude improvement in absolute performance and energy efficiency relative to conventional microprocessors. Despite their promise in both processing performance and efficiency, FPGAs have not yet gained widespread acceptance as mainstream computing devices. A fundamental obstacle to FPGA-based computing today is the FPGA's lack of a common, scalable memory architecture. When developing applications for FPGAs, designers are often directly responsible for crafting the application-specific infrastructure logic that manages and transports data to and from the processing kernels. This infrastructure not only increases design time and effort but will frequently lock a design to a particular FPGA product line, hindering scalability and portability. We propose a new FPGA memory architecture called Connected RAM (CoRAM) to serve as a portable bridge between the distributed computation kernels and the external memory interfaces. In addition to improving performance and efficiency, the CoRAM architecture provides a virtualized memory environment as seen by the hardware kernels to simplify development and to improve an application's portability and scalability.

SRAM design in scaled technologies requires knowledge of phenomena at the process, circuit, and architecture level. Decisions made at various levels of the design hierarchy affect the global figures of merit (FoMs) of an SRAM, such as, performance, power, area, and yield. However, the lack of a quick mechanism to understand the impact of changes at various levels of the hierarchy on global FoMs makes an accurate assessment of SRAM design innovations difficult. Thus, we introduce Virtual Prototyper (ViPro), a tool that helps SRAM designers explore the large design space by rapidly generating optimized virtual prototypes of complete SRAM macros. It does so by allowing designers to describe the SRAM components with varying levels of detail and by incorporating them into a hierarchical model that captures circuit and architectural features of the SRAM to optimize a complete prototype. It generates base-case prototypes that provide starting points for design space exploration, and assesses the impact of process, circuit, and architectural changes on the overall SRAM macro design.

To extend the exponential performance scaling of future chip multiprocessors, improving energy efficiency has become a first-class priority. Single-chip heterogeneous computing has the potential to achieve greater energy efficiency by combining traditional processors with unconventional cores (U-cores) such as custom logic, FPGAs, or GPGPUs. Although U-cores are effective at increasing performance, their benefits can also diminish given the scarcity of projected bandwidth in the future. To understand the relative merits between different approaches in the face of technology constraints, this work builds on prior modeling of heterogeneous multicores to support U-cores. Unlike prior models that trade performance, power, and area using well-known relationships between simple and complex processors, our model must consider the less-obvious relationships between conventional processors and a diverse set of U-cores. Further, our model supports speculation of future designs from scaling trends predicted by the ITRS road map. The predictive power of our model depends upon U-core-specific parameters derived by measuring performance and power of tuned applications on today's state-of-the-art multicores, GPUs, FPGAs, and ASICs. Our results reinforce some current-day understandings of the potential and limitations of U-cores and also provides new insights on their relative merits.

In this paper, we present a vision for the cointegration of deeply scaled complementary metal-oxide-semiconductor (CMOS) and emerging nanoelectronic devices into CMOS-hybrid systems. These hybrid systems will create new functionality, modality and add value to existing CMOS integrated circuits. We describe several new nanoelectronic devices which may enable new dimensions to traditional CMOS circuits and systems that build on CMOS compatible, parallel nanoscale fabrication methods. In addition, we show that the integration of multimodal sensors and nonvolatile memory enables a platform of self-evolving hardware that is able to adapt to fabrication variation, environmental changes, and applications changes on-the-fly.

A designer's intent and knowledge about the critical issues and trade-offs underlying a custom circuit design are implicit in the simulations she sets up for design creation and verification. However, this knowledge is tightly conjoined with technology-specific features and decoupled from the final schematic in traditional design flows. As a result, this knowledge is easily lost when the technology specifics change. This paper presents a technology agnostic simulation environment (TASE), which is a tool that uses simulation templates to capture the designer's knowledge and separate it from the technology-specific components of a simulation. TASE also allows the designer to form groups of related simulations and port them as a unit to a new technology. This allows an actual design schematic to remain tied to the analyses that illuminate the underlying trade-offs and design issues, unlike the case where schematics are ported alone. Giving the designer immediate access to the trade-offs, which are likely to change in new technologies, accelerates the re-design that often must accompany porting of complicated custom circuits. We demonstrate the usefulness of TASE by investigating Read and Write noise margins for a 6T SRAM in predictive technologies down to 16 nm.

Functional full-system simulators are powerful and versatile research tools for accelerating architectural exploration and advanced software development. Their main shortcoming is limited throughput when simulating large multiprocessor systems with hundreds or thousands of processors or when instrumentation is introduced. We propose the ProtoFlex simulation architecture, which uses FPGAs to accelerate full-system multiprocessor simulation and to facilitate high-performance instrumentation. Prior FPGA approaches that prototype a complete system in hardware are either too complex when scaling to large-scale configurations or require significant effort to provide full-system support. In contrast, ProtoFlex virtualizes the execution of many logical processors onto a consolidated number of multiple-context execution engines on the FPGA. Through virtualization, the number of engines can be judiciously scaled, as needed, to deliver on necessary simulation performance at a large savings in complexity. Further, to achieve low-complexity full-system support, a hybrid simulation technique called transplanting allows implementing in the FPGA only the frequently encountered behaviors, while a software simulator preserves the abstraction of a complete system. We have created a first instance of the ProtoFlex simulation architecture, which is an FPGA-based, full-system functional simulator for a 16-way UltraSPARC III symmetric multiprocessor server, hosted on a single Xilinx Virtex-II XCV2P70 FPGA. On average, the simulator achieves a 38x speedup (and as high as 49×) over comparable software simulation across a suite of applications, including OLTP on a commercial database server. We also demonstrate the advantages of minimal-overhead FPGA-accelerated instrumentation through a CMP cache simulation technique that runs orders-of-magnitude faster than software.

Functional full-system simulators are powerful and versatile research tools for accelerating architectural exploration and advanced software development. Their main shortcoming is limited throughput when simulating systems with hundreds of processors or more. To overcome this bottleneck, we propose the PROTOFLEX simulation architecture, which uses FPGAs to accelerate simulation. Prior FPGA approaches that prototype a complete system in hardware are either too complex when scaling to large-scale configurations or require significant effort to provide full-system support. In contrast, PROTOFLEX reduces complexity by virtualizing the execution of many logical processors onto a consolidated set of multiple-context execution engines on the FPGA. Through virtualization, the number of engines can be judiciously scaled, as needed, to deliver on necessary simulation performance. To achieve low-complexity full-system support, a hybrid simulation technique called transplanting allows implementing in the FPGA only the frequently encountered behaviors, while a software simulator preserves the abstraction of a complete system

Well-designed circuits are one key ldquoinsulatingrdquo layer between the increasingly unruly behavior of scaled complementary metal-oxide-semiconductor devices and the systems we seek to construct from them. As we move forward into the nanoscale regime, circuit design is burdened to ldquohiderdquo more of the problems intrinsic to deeply scaled devices. How this is being accomplished is the subject of this paper. We discuss new techniques for logic circuits and interconnect, for memory, and for clock and power distribution. We survey work to build accurate simulation models for nanoscale devices. We discuss the unique problems posed by nanoscale lithography and the role of geometrically regular circuits as one promising solution. Finally, we look at recent computer-aided design efforts in modeling, analysis, and optimization for nanoscale designs with ever increasing amounts of statistical variation.

PROTOFLEX is an FPGA-accelerated hybrid simulation/emulation platform designed to support large-scale multiprocessor hardware and software research. Unlike prior attempts at FPGA multiprocessor system emulators, PROTOFLEx emulates full-system fidelityi.e., runs stock commercial operating systems with I/O support. This is accomplished without undue effort by leveraging a hybrid emulation technique called transplanting. Our transplant technology uses FPGAs to accelerate only common-case behaviors while relegating infrequent, complex behaviors (e.g., I/O devices) to software simulation. By working in concert with existing full-system simulators, transplanting avoids the costly and unnecessary construction of the entire target system in FPGA. We report preliminary findings from a working hybrid PROTOFLEx emulator of an UltraSPARC workstation running Solaris 8. We have also started developing a novel multiprocessor emulation approach that interleaves the execution of many (10s to 100s) processor contexts onto a shared emulation engine. This approach decouples the scale and complexity of the FPGA hostfrom the simulated system size but neverthe-less enables us to scale the desired emulation performance by the number of emulation engines used. Together, the transplant and interleaving techniques will enable us to develop full-system FPGA emulators of up to thousands of processors without an overwhelming development effort.

In deep sub-micron ICs, growing amounts of on-die memory and scaling effects make embedded memories increasingly vulnerable to reliability and yield problems. As scaling progresses, soft and hard errors in the memory system will increase and single error events are more likely to cause large-scale multi- bit errors. However, conventional memory protection techniques can neither detect nor correct large-scale multi-bit errors without incurring large performance, area, and power overheads. We propose two-dimensional (2D) error coding in embedded memories, a scalable multi-bit error protection technique to improve memory reliability and yield. The key innovation is the use of vertical error coding across words that is used only for error correction in combination with conventional per-word horizontal error coding. We evaluate this scheme in the cache hierarchies of two representative chip multiprocessor designs and show that 2D error coding can correct clustered errors up to 32times32 bits with significantly smaller performance, area, and power overheads than conventional techniques.

Concern about the performance of wires wires in scaled technologies has led to research exploring other communication methods. This paper examines wire and gate delays as technologies migrate from 0.18-/spl mu/m to 0.035-/spl mu/m feature sizes to better understand the magnitude of the the wiring problem. Wires that shorten in length as technologies scale have delays that either track gate delays or grow slowly relative to gate delays. This result is good news since these "local" wires dominate chip wiring. Despite this scaling of local wire performance, computer-aided design (CAD) tools must still become move sophisticated in dealing with these wires. Under scaling, the total number of wires grows exponentially, so CAD tools will need to handle an ever-growing percentage of all the wires in order to keep designer workloads constant. Global wires present a more serious problem to designers. These are wires that do not scale in length since they communicate signals across the chip. The delay of these wives will remain constant if repeaters are used meaning that relative to gate delays, their delays scale upwards. These increased delays for global communication will drive architectures toward modular designs with explicit global latency mechanisms.

Trends in VLSI technology scaling demand that future computing devices be narrowly focused to achieve high performance and high efficiency, yet also target the high volumes and low costs of widely applicable general purpose designs. To address these conflicting requirements, we propose a modular reconfigurable architecture called Smart Memories, targeted at computing needs in the 0.1 /spl mu/m technology generation. A Smart Memories chip is made up of many processing tiles, each containing local memory, local interconnect, and a processor core. For efficient computation under a wide class of possible applications, the memories, the wires, and the computational model can all be altered to match the applications. To show the applicability of this design, two very different machines at opposite ends of the architectural spectrum, the Imagine stream processor and the Hydra speculative multiprocessor, are mapped onto the Smart Memories computing substrate. Simulations of the mappings show that the Smart Memories architecture can successfully map these architectures with only modest performance degradation.

Interconnect scaling to deep submicron processes presents many challenges to today's CAD flows. A recent analysis by D. Sylvester and K. Keutzer (1998) examined the behavior of average length wires under scaling, and controversially concluded that current CAD tools are adequate for future module-level designs. We show that average length wire scaling is sensitive to the technology assumptions, although the change in their behavior is small under all reasonable scaling assumptions. However, examining only average length wires is optimistic, since long wires are the ones that primarily cause CAD tool exceptions. In a module of fixed complexity, under both optimistic and pessimistic scaling assumptions, the number of long wires will increase slowly with scaling. More importantly, as the overall die capacity grows exponentially, the number of modules and thus the total number of wires in a design will also increase exponentially. Thus, if the design team size and per-designer workload is to remain relatively constant, future CAD tools will need to handle long wires much better than current tools to reduce the percentage of wires that require designer intervention.