Recent work has demonstrated the effectiveness of using symbolic simulation to perform hardware software co-analysis on an application-processor pair and developed a variety of hardware and software design techniques and optimizations, ranging from providing system security guarantees to automated generation of application-specific bespoke processors. Despite their potential benefits, current state-of-the-art symbolic simulation tools for hardware-software co-analysis are restricted in their applicability, since prior work relies on a costly process of building a custom simulation tool for each processor design to be simulated. Furthermore, prior work does not describe how to extend the symbolic analysis technique to other processor designs.

Machine learning based prediction of acute coronary syndrome positive patients is becoming increasingly common. However, there is little to no research in terms of assessing fairness and interpretability of these methods. This study discusses methodologies to reduce fairness related disparities in acute coronary syndrome predictions made by machine learning algorithms for different demographics sections of the population. Additionally, existing interpretability technique called LIME is used to understand predictions made using fairness methodologies. Our analysis show that discussed methods have: (1) potential to reduce treatment disparities; (2) generalize well to different healthcare setting in the United States; and (3) reduce overall false negative rates by 0.34 and improve AUROC score by 0.11 when compared to algorithms like XGBoost.

Symbolic simulation and symbolic execution techniques have long been used for verifying designs and testing software. Recently, using symbolic hardware-software co-analysis to characterize unused hardware resources across all possible executions of an application running on a processor has been leveraged to enable application-specific analysis and optimization techniques. Like other symbolic simulation techniques, symbolic hardware-software co-analysis does not scale well to complex applications, due to an explosion in the number of execution paths that must be analyzed to characterize all possible executions of an application. To overcome this issue, prior work proposed a scalable approach by maintaining conservative states of the system at previously-visited locations in the application. However, this approach can be too pessimistic in determining the exercisable subset of resources of a hardware design. In this paper, we propose a technique for performing symbolic co-analysis of an application on a processor's netlist by identifying, propagating, and imposing constraints from the software level onto the gate-level simulation. This produces a more precise, less pessimistic estimate of the gates that an application can exercise when executing on a processor, while guaranteeing coverage of all possible gates that the application can exercise. This also reduces the simulation time of the analysis, significantly, by eliminating the need to explore many simulation paths in the application. Compared to the state-of-art analysis based on conservative states, our constrained approach reduces the number of gates identified as exercisable by up to 34.98%, 11.52% on average, and analysis runtime by up to 84.61%, 43.83% on average.

As the diversity of computing workloads and customers continues to increase, so does the need to customize hardware at low cost for different computing needs. This work focuses on automatic customization of a given hardware, available as a soft or firm IP, through eliminating unneeded or undesired instruction set architecture (ISA) instructions. We present a property-based framework for automatically generating reduced-ISA hardware. Our framework directly operates on a given arbitrary RTL or gate-level netlist, uses property checking to identify gates that are guaranteed to not toggle if only a reduced ISA needs to be supported, and automatically eliminates these untoggleable gates to generate a new design. We show a 14% gate count reduction when the Ibex [19] core is optimized using our framework for the instructions required by a set of embedded (MiBench) workloads. Reduced-ISA versions generated by our framework that support a limited set of ISA extensions and which cannot be generated using Ibex’s parameterization options provide 10%47% gate count reduction. For an obfuscated Cortex M0 netlist optimized to support the instructions in the MiBench benchmarks, we observe a 20% area reduction and 18% gate count reduction compared to the baseline core, demonstrating applicability of our framework to obfuscated designs. We demonstrate the scalability of our approach by applying our framework to a 100,000-gate RIDECORE [21] design, showing a 14%17% gate count reduction.

Extensive research has been carried out to improve cache replacement policies, yet designing an efficient cache replacement policy that incurs low hardware overhead remains a challenging and time-consuming task. Given the surging interest in applying machine learning (ML) to challenging computer architecture design problems, we use ML as an offline tool to design a cost-effective cache replacement policy. We demonstrate that ML is capable of guiding and expediting the generation of a cache replacement policy that is competitive with state-of-the-art hand-crafted policies. In this work, we use Reinforcement Learning (RL) to learn a cache replacement policy. After analyzing the learned model, we are able to focus on a few critical features that might impact system performance. Using the insights provided by RL, we successfully derive a new cache replacement policy – Reinforcement Learned Replacement (RLR). Compared to the state-of-the-art policies, RLR has low hardware overhead, and it can be implemented without needing to modify the processor’s control and data path to propagate information such as program counter. On average, RLR improves single-core and four-core system performance by 3.25% and 4.86% over LRU, with an overhead of 16.75KB for 2MB last-level cache (LLC) and 67KB for 8MB LLC.

Statistical modeling of outcomes based on a patient's presenting symptoms (symptomatology) can help deliver high quality care and allocate essential resources, which is especially important during the COVID-19 pandemic. Patient symptoms are typically found in unstructured notes, and thus not readily available for clinical decision making. In an attempt to fill this gap, this study compared two methods for symptom extraction from Emergency Department (ED) admission notes. Both methods utilized a lexicon derived by expanding The Center for Disease Control and Prevention's (CDC) Symptoms of Coronavirus list. The first method utilized a word2vec model to expand the lexicon using a dictionary mapping to the Uni ed Medical Language System (UMLS). The second method utilized the expanded lexicon as a rule-based gazetteer and the UMLS. These methods were evaluated against a manually annotated reference (f1-score of 0.87 for UMLS-based ensemble; and 0.85 for rule-based gazetteer with UMLS). Through analyses of associations of extracted symptoms used as features against various outcomes, salient risks among the population of COVID-19 patients, including increased risk of in-hospital mortality (OR 1.85, p-value < 0.001), were identified for patients presenting with dyspnea. Disparities between English and non-English speaking patients were also identified, the most salient being a concerning finding of opposing risk signals between fatigue and in-hospital mortality (non-English: OR 1.95, p-value = 0.02; English: OR 0.63, p-value = 0.01). While use of symptomatology for modeling of outcomes is not unique, unlike previous studies this study showed that models built using symptoms with the outcome of in-hospital mortality were not significantly different from models using data collected during an in-patient encounter (AUC of 0.9 with 95% CI of [0.88, 0.91] using only vital signs; AUC of 0.87 with 95% CI of [0.85, 0.88] using only symptoms). These findings indicate that prognostic models based on symptomatology could aid in extending COVID-19 patient care through telemedicine, replacing the need for in-person options. The methods presented in this study have potential for use in development of symptomatology-based models for other diseases, including for the study of Post-Acute Sequelae of COVID-19 (PASC).

Ultra-low-power (ULP) chipsets are in higher demand than ever due to the proliferation of ULP embedded systems to support growing applications like the Internet of Things (IoT), wearables and sensor networks. Since ULP systems are also cost constrained, they tend to employ general purpose processors (GPPs) rather than more energy-efficient ASICs, even though they typically run a single application for the lifetime of the system. Prior work showed that it is possible to reduce the operating voltage and thus the power of such systems without reducing the frequency, since the fixed software stack of a system typically only exercises a subset of a processor's paths, and unexercised paths need not meet timing constraints for the system to work correctly. In this context, we find additional scope for power reduction by intelligently optimizing the processor design based on the system's application-specific activity characteristics to allow an even lower safe operating voltage. We demonstrate automated techniques that maximize the application-specific voltage reduction for a system, resulting in 35% additional power savings, on average, compared to the application-specific minimum voltage before optimization and 48% total power savings compared to the original design at nominal voltage.

Many emerging applications such as IoT, wearables, implantables, and sensor networks are power- and energy-constrained. These applications rely on ultra-low-power processors that have rapidly become the most abundant type of processor manufactured today. In the ultra-low-power embedded systems used by these applications, peak power and energy requirements are the primary factors that determine critical system characteristics, such as size, weight, cost, and lifetime. While the power and energy requirements of these systems tend to be application-specific, conventional techniques for rating peak power and energy cannot accurately bound the power and energy requirements of an application running on a processor, leading to over-provisioning that increases system size and weight. In this paper, we present an automated technique that performs hardware-software co-analysis of the application and ultra-low-power processor in an embedded system to determine application-specific peak power and energy requirements. Our technique provides more accurate, tighter bounds than conventional techniques for determining peak power and energy requirements, reporting 15% lower peak power and 17% lower peak energy, on average, than a conventional approach based on profiling and guardbanding. Compared to an aggressive stressmark-based approach, our technique reports power and energy bounds that are 26% and 26% lower, respectively, on average. Also, unlike conventional approaches, our technique reports guaranteed bounds on peak power and energy independent of an application's input set. Tighter bounds on peak power and energy can be exploited to reduce system size, weight, and cost.

The increasingly-stringent power and energy requirements of emerging embedded applications have led to a strong recent interest in aggressive power gating techniques. Conventional techniques for aggressive power gating perform module-based power gating in processors, where power domains correspond to RTL modules. We observe that there can be significant power benefits from module-oblivious power gating, where power domains can include an arbitrary set of gates, possibly from multiple RTL modules. However, since it is not possible to infer the activity of module-oblivious power domains from software alone, conventional software-based power management techniques cannot be applied for module-oblivious power gating in processors. Also, since module-oblivious domains are not encapsulated with a well-defined port list and functionality like RTL modules, hardware-based management of module-oblivious domains is prohibitively expensive. In this paper, we present a technique for low-cost management of moduleoblivious power domains in embedded processors. The technique involves symbolic simulation-based co-analysis of a processor's hardware design and a software binary to derive profitable and safe power gating decisions for a given set of module-oblivious domains when the software binary is run on the processor. Our technique is automated, does not require programmer intervention, and incurs low management overhead. We demonstrate that module-oblivious power gating based on our technique reduces leakage energy by 2× with respect to state-of-the-art aggressive module-based power gating for a common embedded processor.

As the overheads for ensuring the correctness of electronic designs continue to increase with continued technology scaling and increased variability, better-than-worst-case (BTWC) design has gained significant attention. Many BTWC design techniques utilize dynamic timing and activity information for design analysis and optimization. These techniques rely on path-based analysis that enumerates the exercised paths in a design as targets for analysis and optimization. However, path-based dynamic analysis techniques are not scalable and cannot be used to analyze full processors and full applications. On the other hand, graph-based techniques like those that form the foundational building blocks of electronic design automation tools are scalable and can efficiently analyze large designs. In this paper, we extend graph-based analysis to provide the fundamental dynamic analysis tools necessary for BTWC design, analysis, and optimization. Specifically, we present scalable graph-based techniques to report the N-worst exercised paths in a design for three metrics - timing criticality (slack), activity (toggle count), and activity subject to delay constraints. Compared to existing path-based techniques, our scalable dynamic analysis techniques improve average performance by 977 ×, 163 ×, and 113 x, respectively, and enable scalable analysis for a full processor design running full applications.

A large number of emerging applications such as implantables, wearables, printed electronics, and IoT have ultra-low area and power constraints. These applications rely on ultra-low-power general purpose microcontrollers and microprocessors, making them the most abundant type of processor produced and used today. While general purpose processors have several advantages, such as amortized development cost across many applications, they are significantly over-provisioned for many area- and power-constrained systems, which tend to run only one or a small number of applications over their lifetime. In this paper, we make a case for bespoke processor design, an automated approach that tailors a general purpose processor IP to a target application by removing all gates from the design that can never be used by the application. Since removed gates are never used by an application, bespoke processors can achieve significantly lower area and power than their general purpose counterparts without any performance degradation. Also, gate removal can expose additional timing slack that can be exploited to increase area and power savings or performance of a bespoke design. Bespoke processor design reduces area and power by 62% and 50%, on average, while exploiting exposed timing slack improves average power savings to 65%.

The growing movement to connect literally everything to the internet (internet of things or IoT) through ultra-low-power embedded microprocessors poses a critical challenge for information security. Gate-level tracking of information flows has been proposed to guarantee information flow security in computer systems. However, such solutions rely on non-commodity, secure-by-design processors. In this work, we observe that the need for secure-by-design processors arises because previous works on gate-level information flow tracking assume no knowledge of the application running in a system. Since IoT systems typically run a single application over and over for the lifetime of the system, we see a unique opportunity to provide application-specific gate-level information flow security for IoT systems. We develop a gate-level symbolic analysis framework that uses knowledge of the application running in a system to efficiently identify all possible information flow security vulnerabilities for the system. We leverage this information to provide security guarantees on commodity processors. We also show that security vulnerabilities identified by our analysis framework can be eliminated through software modifications at 15% energy overhead, on average, obviating the need for secure-by-design hardware. Our framework also allows us to identify and eliminate only the vulnerabilities that an application is prone to, reducing the cost of information flow security by 3.3× compared to a software-based approach that assumes no application knowledge.

Many emerging applications such as the Internet of Things, wearables, implantables, and sensor networks are constrained by power and energy. These applications rely on ultra-low-power processors that have rapidly become the most abundant type of processor manufactured today. In the ultra-low-power embedded systems used by these applications, peak power and energy requirements are the primary factors that determine critical system characteristics, such as size, weight, cost, and lifetime. While the power and energy requirements of these systems tend to be application specific, conventional techniques for rating peak power and energy cannot accurately bound the power and energy requirements of an application running on a processor, leading to overprovisioning that increases system size and weight. In this article, we present an automated technique that performs hardware–software coanalysis of the application and ultra-low-power processor in an embedded system to determine application-specific peak power and energy requirements. Our technique provides more accurate, tighter bounds than conventional techniques for determining peak power and energy requirements. Also, unlike conventional approaches, our technique reports guaranteed bounds on peak power and energy independent of an application’s input set. Tighter bounds on peak power and energy can be exploited to reduce system size, weight, and cost.

Synthesis tools for approximate sequential circuits require the ability to quickly, efficiently, and automatically characterize and bound the errors produced by the circuits. Previous approaches to characterize errors in approximate sequential circuits have been based on simulations spanning all cycles of a sequential computation. These approaches, however, are not scalable and only accommodate small circuit modules and short computation times. In this paper, we observe that the statistical properties of errors in many approximate sequential circuits follow patterns that can be easily learned. Therefore, statistical error characteristics of these approximate sequential circuits can be predicted with high accuracy using only a few cycles of characterization data. Based on this novel observation, we propose a methodology for predicting error statistics in approximate sequential circuits that is accurate, fully automated, and has significantly lower overhead than prior approaches. Our methodology is robust to changes in predicted error metrics, circuit input distributions, and types of approximate hardware modules used in approximate circuits. We demonstrate the accuracy and scalability of our approach over a range of sequential circuits. On average, prediction inaccuracy is less than 2% and error characterization time is reduced by 99% compared to a simulation-based approach.

Many emerging applications such as the internet of things, wearables, and sensor networks have ultra-low-power requirements. At the same time, cost and programmability considerations dictate that many of these applications will be powered by general purpose embedded microprocessors and microcontrollers, not ASICs. In this paper, we exploit a new opportunity for improving energy efficiency in ultralow-power processors expected to drive these applications -- dynamic timing slack. Dynamic timing slack exists when an embedded software application executed on a processor does not exercise the processor's static critical paths. In such scenarios, the longest path exercised by the application has additional timing slack which can be exploited for power savings at no performance cost by scaling down the processor's voltage at the same frequency until the longest exercised paths just meet timing constraints. Paths that cannot be exercised by an application can safely be allowed to violate timing constraints. We show that dynamic timing slack exists for many ultra-low-power applications and that exploiting dynamic timing slack can result in significant power savings for any ultra-low-power processors. We also present an automated methodology for identifying dynamic timing slack and selecting a safe operating point for a processor and a particular embedded software. Our approach for identifying and exploiting dynamic timing slack is non-speculative, requires no programmer intervention and little or no hardware support, and demonstrates potential power savings of up to 32%, 25% on average, over a range of embedded applications running on a common ultra-low-power processor, at no performance cost.

In light of increasing energy overheads required to guarantee correctness as variations increase with continued technology scaling, better-than-worst-case (BTWC) design has become a hot topic. Several BTWC design techniques utilize dynamic information like path activity when optimizing a design and rely on path-based analysis to determine the dynamic slack distribution of a workload running on a processor and subsequently optimize a design. In this paper, we show that path-based techniques are not scalable, due to the enormous number of paths in modern designs, and can also result in incorrect results. We propose a graph-based technique for performing dynamic timing and activity analysis of a workload on a processor that addresses the limitations of path-based techniques. Our tool has significantly lower runtime and memory requirements than path-based tools. Consequently, we can perform analysis for larger designs over longer time windows in a shorter amount of time. We also propose two optimizations that improve the performance of our tool.

Binarization plays an important role in document image processing, particularly in degraded document images. Among all local adaptive image thresholding algorithms, the Nick method has shown excellent binarization performance for degraded document images. However, local image thresholding algorithms, including the Nick method, are computationally intensive, requiring significant time to process input images. In this paper, we propose three CUDA GPU parallel implementations of the Nick local image thresholding algorithm for faster binarization of large images. Our experimental results show that the GPU-accelerated implementations of the Nick method can achieve up to 150x performance speedup on a GeForce GTX 480 compared to its optimized sequential implementation.

Software-based path delay fault testing (SPDFT) has been used to identify faulty chips that cannot meet timing constraints due to gross delay defects. In this paper, we propose using SPDFT for a new purpose -- aggressively selecting the operating point of a variation-affected design. In order to use SPDFT for this purpose, test routines must provide high coverage of potentially-critical paths and must have low dynamic performance overhead. We describe how to apply SPDFT for selecting an energy-efficient operating point for a variation-affected processor and demonstrate that our test routines achieve ample coverage and low overhead.

Aggressive requirements for low power and high performance in VLSI designs have led to increased interest in approximate computation. Approximate hardware modules can achieve improved energy efficiency compared to accurate hardware modules. While a number of previous works have proposed hardware modules for approximate arithmetic, these works focus on solitary approximate arithmetic operations. To utilize the benefit of approximate hardware modules, CAD tools should be able to quickly and accurately estimate the output quality of composed approximate designs. A previous work [10] proposes an interval-based approach for evaluating the output quality of certain approximate arithmetic designs. However, their approach uses sampled error distributions to store the characterization data of hardware, and its accuracy is limited by the number of intervals used during characterization. In this work, we propose an approach for output quality estimation of approximate designs that is based on a lookup table technique that characterizes the statistical properties of approximate hardwares and a regression-based technique for composing statistics to formulate output quality. These two techniques improve the speed and accuracy for several error metrics over a set of multiply-accumulator testcases. Compared to the interval-based modeling approach of [10], our approach for estimating output quality of approximate designs is 3.75ß more accurate for comparable runtime on the testcases and achieves 8.4ß runtime reduction for the error composition flow. We also demonstrate that our approach is applicable to general testcases.

Due to their large memory capacities, many modern servers require chipkill correct, an advanced type of memory error detection and correction, to meet their reliability requirements. However, existing chipkill-correct solutions incur high power or storage overheads, or both because they use dedicated error-correction resources per codeword to perform error correction. This requires high overhead for correction and results in high overhead for error detection. We propose a novel chipkill-correct solution, multi-line error correction, that uses resources shared across multiple lines in memory for error correction to reduce the overhead of both error detection and correction. Our evaluations show that the proposed solution reduces memory power by a mean of 27%, and up to 38% with respect to commercial solutions, at a cost of 0.4% increase in storage overhead and minimal impact on reliability.

Escalating variations in modern CMOS designs have become a threat to Moore’s law. In light of the increasing costs of standard worst-case design practices, timing speculation has become a popular approach for dealing with static and dynamic non-determinism and increasing yield. Timing speculative architectures allow conservative guardbands to be relaxed, increasing efficiency at the expense of occasional errors, which are corrected or tolerated by an error resilience mechanism. Previous work has proposed circuit- or design-level optimizations that manipulate the error rate behavior of a design to increase the efficiency of timing speculation. In this article, we investigate whether architectural optimizations can also manipulate error rate behavior to significantly increase the effectiveness of timing speculation. To this end, we demonstrate how error rate behavior indeed depends on processor architecture and that architectural optimizations can be used to manipulate the error rate behavior of a processor. Using timing speculation-aware architectural optimizations, we demonstrate enhanced overscaling and up to 29% additional energy savings for processors that employ Razor-based timing speculation.

Control and memory divergence between threads within the same execution bundle, or warp, have been shown to cause significant performance bottlenecks for GPU applications. In this paper, we exploit the observation that many GPU applications exhibit error tolerance to propose branch and data herding. Branch herding eliminates control divergence by forcing all threads in a warp to take the same control path. Data herding eliminates memory divergence by forcing each thread in a warp to load from the same memory block. To safely and efficiently support branch and data herding, we propose a static analysis and compiler framework to prevent exceptions when control and data errors are introduced, a profiling framework that aims to maximize performance while maintaining acceptable output quality, and hardware optimizations to improve the performance benefits of exploiting error tolerance through branch and data herding. Our software implementation of branch herding on NVIDIA GeForce GTX 480 improves performance by up to 34% (13%, on average) for a suite of NVIDIA CUDA SDK and Parboil benchmarks. Our hardware implementation of branch herding improves performance by up to 55% (30%, on average). Data herding improves performance by up to 32% (25%, on average). Observed output quality degradation is minimal for several applications that exhibit error tolerance, especially for visual computing applications.

The proliferation of embedded systems and mobile devices has created an increasing demand for low-energy hardware. Dynamic voltage and frequency scaling (DVFS) is a popular energy reduction technique that allows a hardware design to reduce average power consumption while still enabling the design to meet a high-performance target when necessary. To conserve energy, many DVFS-based embedded and mobile devices often spend a large fraction of their lifetimes in a low-power mode. However, DVFS designs produced by conventional multimode CAD flows tend to have significant energy overheads when operating outside of the peak performance mode, even when they are operating in a low-power mode. A dedicated core can be added for low-energy operation, but has a high cost in terms of area and leakage. In this paper, we explore the DVFS design space to identify the factors that affect DVFS efficiency. Based on our insights, we propose two design-level techniques to enhance the energy efficiency of DVFS for energy constrained systems. First, we present a context-aware DVFS design flow that considers the intrinsic characteristics of the hardware design, as well as the operating scenario—including the relative amounts of time spent in different modes, the range of performance scalability, and the target efficiency metric—to optimize the design for maximum energy efficiency. We also present a selective replication-based DVFS design methodology that identifies hardware modules for which context-aware multimode design may be inefficient and creates dedicated module replicas for different operating modes for such modules. We show that context-aware design can reduce average power by up to 20% over a conventional multimode design flow. Selective replication can reduce average power by an additional 4%. We also use the generated insights to identify microarchitectural decisions that impact DVFS efficiency. We show that the benefits from the proposed design-level techniques increase when microarchitectural transformations are allowed.

Control and memory divergence between threads in the same execution bundle, or warp, can significantly throttle the performance of GPU applications. We exploit the observation that many GPU applications exhibit error tolerance to propose branch and data herding. Branch herding eliminates control divergence by forcing all threads in a warp to take the same control path. Data herding eliminates memory divergence by forcing each thread in a warp to load from the same memory block. To safely and efficiently support branch and data herding, we propose a static analysis and compiler framework to prevent exceptions when control and data errors are introduced, a profiling framework that aims to maximize performance while maintaining acceptable output quality, and hardware optimizations to improve the performance benefits of exploiting error tolerance through branch and data herding. Our software implementation of branch herding on NVIDIA GeForce GTX 480 improves performance by up to 34% (13%, on average) for a suite of NVIDIA CUDA SDK and Parboil benchmarks. Our hardware implementation of branch herding improves performance by up to 55% (30%, on average). Data herding improves performance by up to 32% (25%, on average). Observed output quality degradation is minimal for several applications that exhibit error tolerance, especially for visual computing applications.

Timing speculation is a promising technique for improving microprocessor yield, in field reliability, and energy efficiency. Previous evaluations of the energy efficiency benefits of timing speculation have either been based on code compiled for a traditional target [2]--a processor that produces no errors, or code that relies on additional hardware support [6]. In this paper, we advocate that binaries for timing speculative processors should be optimized differently than those for conventional processors to maximize the energy benefits of timing speculation. Since the program binary determines the utilization pattern of the processor, which in turn influences the error rate of the processor and the energy efficiency of timing speculation, binary optimizations for timing speculative processors should attempt to manipulate the utilization of different microarchitectural units based on their likelihood of causing errors. An exploration of targeted and standard compiler optimizations demonstrates that significant energy benefits are possible from TS-aware binary optimization.

Much recent research [8, 6, 7] suggests significant power and energy benefits of relaxing correctness constraints in future processors. Such processors with relaxed constraints have often been referred to as stochastic processors [10, 15, 11]. In this paper we present three approaches for building applications for such processors. The first approach relies on relaxing the correctness of the application based upon an analysis of application characteristics. The second approach relies upon detecting and then correcting faults within the application as they arise. The third approach transforms applications into more error tolerant forms. In this paper, we show how these techniques that enhance or exploit the error tolerance of applications can yield significant power and energy benefits when computed on stochastic processors.

Since the onset of pipelined processors, balancing the delay of the microarchitectural pipeline stages such that each microarchitectural pipeline stage has an equal delay has been a primary design objective, as it maximizes instruction throughput. Unfortunately, this causes significant energy inefficiency in processors, as each microarchitectural pipeline stage gets the same amount of time to complete, irrespective of its size or complexity. For power-optimized processors, the inefficiency manifests itself as a significant imbalance in power consumption of different microarchitectural pipestages. In this paper, rather than balancing processor pipelines for delay, we propose the concept of power balanced pipelines - i.e., processor pipelines in which different delays are assigned to different microarchitectural pipestages to reduce the power disparity between the stages while guaranteeing the same processor frequency/performance. A specific implementation of the concept uses cycle time stealing [19] to deliberately redistribute cycle time from low-power pipeline stages to power-hungry stages, relaxing their timing constraints and allowing them to operate at reduced voltages or use smaller, less leaky cells. We present several static and dynamic techniques for power balancing and demonstrate that balancing pipeline power rather than delay can result in 46% processor power reduction with no loss in processor throughput for a full FabScalar processor over a power-optimized baseline. Benefits are comparable over a Fabscalar baseline where static cycle time stealing is used to optimize achieved frequency. Power savings increase at lower operating frequencies. To the best of our knowledge, this is the first such work on microarchitecture-level power reduction that guarantees the same performance.

Conventional computer-aided design (CAD) methodologies optimize a processor module for correct operation and prohibit timing violations during nominal operation. We propose recovery-driven design, a design approach that optimizes a processor module for a target timing error rate (ER) instead of correct operation. The target ER is chosen based on how many errors can be gainfully tolerated by a hardware or software error resilience mechanism. We show that significant power benefits are possible from a recovery-driven design approach that deliberately allows errors caused by voltage overscaling to occur during nominal operation, while relying on an error resilience technique to tolerate these errors. We present a detailed evaluation and analysis of such a CAD methodology that minimizes the power of a processor module for a target ER. We show how this design-level methodology can be extended to design recovery-driven processors—processors that are optimized to take advantage of hardware or software error resilience. We also discuss a gradual slack recovery-driven design approach that optimizes for a range of ERs to create soft processors—processors that have graceful failure characteristics and the ability to trade throughput or output quality for additional energy savings over a range of ERs. We demonstrate significant power benefits over conventional design—11.8% on average over all modules and ER targets, and up to 29.1% for individual modules. Processor-level benefits were 19.0%, on average. Benefits increase when recovery-driven design is coupled with an error resilience mechanism or when the number of available voltage domains increases.

Escalating variations in modern CMOS designs have become a threat to Moore's law. While previous works have proposed techniques for tolerating variations by trading reliability for reduced voltage (energy) [10], the benefits of such techniques are limited, because voltage/reliability tradeoffs in conventional processors often introduce more errors than can be gainfully tolerated [14]. Recent work has proposed circuit and design-level optimizations [14, 15]that manipulate the error rate behavior of a design to increase the potential for energy savings from voltage/reliability tradeoffs. In this paper, we investigate whether architectural optimizations can also manipulate error rate behavior to significantly increase the energy savings from voltage/reliability tradeoffs. To this end, we demonstrate how error rate behavior indeed depends on processor architecture, and that architectural optimizations can be used to manipulate the error rate behavior of a processor. We show that architectural optimizations can significantly enhance voltage/reliability tradeoffs, achieving up to 29% additional energy savings for processors that employ Razor-based error resilience.

As device sizes shrink, device-level manufacturing challenges have led to increased variability in physical circuit characteristics. Exponentially increasing circuit density has not only brought about concerns in the reliable manufacturing of circuits, but has also exaggerated variations in dynamic circuit behavior. The resulting uncertainty in performance, power, and reliability imposed by compounding static and dynamic non-determinism threatens to halt the continuation of Moore's law, which has been arguably the primary driving force behind technology and innovation for decades. As the marginal benefits of technology scaling continue to languish, a new vision for stochastic computing has begun to emerge. Rather than hiding variations under expensive guardbands, designers have begun to relax traditional correctness constraints and deliberately expose hardware variability to higher levels of the compute stack, thus tapping into potentially significant performance and energy benefits, while exploiting software and hardware error resilience to tolerate errors. In this paper, we present our vision for design, architecture, compiler, and application-level stochastic computing techniques that embrace errors in order to ensure the continued viability of semiconductor scaling.

Negative Bias Temperature Instability (NBTI) has become an important reliability issue in modern semiconductor processes. Recent work has attempted to address NBTI-induced degradation at the architecture level. However, such work has relied on device-level analytical models that, we argue, are limited in their flexibility to model the impact of architecture-level techniques on NBTI degradation. In this paper, we propose a flexible numerical model for NBTI degradation that can be adapted to better estimate the impact of architecture-level techniques on NBTI degradation. Our model is a numerical solution to the reaction-diffusion equations describing NBTI degradation that has been parameterized to model the impact of dynamic voltage scaling, averaging effects across logic paths, power gating, and activity management We use this model to understand the effectiveness of different classes of architecture-level techniques that have been proposed to mitigate the effects of NBTI. We show that the potential benefits from these techniques are, for the most part, smaller than what has been previously suggested, and that guardbanding may still be an efficient way to deal with aging.

Modern digital IC designs have a critical operating point, or “wall of slack”, that limits voltage scaling. Even with an error-tolerance mechanism, scaling voltage below a critical voltage - so-called overscaling - results in more timing errors than can be effectively detected or corrected. This limits the effectiveness of voltage scaling in trading off system reliability and power. We propose a design-level approach to trading off reliability and voltage (power) in, e.g., microprocessor designs. We increase the range of voltage values at which the (timing) error rate is acceptable; we achieve this through techniques for power-aware slack redistribution that shift the timing slack of frequently-exercised, near-critical timing paths in a power- and area-efficient manner. The resulting designs heuristically minimize the voltage at which the maximum allowable error rate is encountered, thus minimizing power consumption for a prescribed maximum error rate and allowing the design to fail more gracefully. Compared with baseline designs, we achieve a maximum of 32.8% and an average of 12.5% power reduction at an error rate of 2%. The area overhead of our techniques, as evaluated through physical implementation (synthesis, placement and routing), is no more than 2.7%.

Conventional CAD methodologies optimize a processor module for correct operation, and prohibit timing violations during nominal operation. In this paper, we propose recovery-driven design, a design approach that optimizes a processor module for a target timing error rate instead of correct operation. We show that significant power benefits are possible from a recovery-driven design flow that deliberately allows errors caused by voltage overscaling ([10],[3]) to occur during nominal operation, while relying on an error recovery technique to tolerate these errors. We present a detailed evaluation and analysis of such a CAD methodology that minimizes the power of a processor module for a target error rate. We demonstrate power benefits of up to 25%, 19%, 22%, 24%, 20%, 28%, and 20% versus traditional P&R at error rates of 0.125%, 0.25%, 0.5%, 1%, 2%, 4%, and 8%, respectively. Coupling recovery-driven design with an error recovery technique enables increased efficiency and additional power savings.

Future microprocessors increasingly rely on an unreliable CMOS fabric due to aggressive scaling of voltage and frequency, and shrinking design margins. Fortunately, many emerging applications can tolerate computational errors caused by hardware unreliabilities, at least during certain execution intervals. In this paper, we propose scalable stochastic processors, a computing platform for error-tolerant applications that is able to scale gracefully according to performance demands and power constraints while producing outputs that are, in the worst case, stochastically correct. Scalability is achieved by exposing to the application layer multiple functional units that differ in their architecture but share functionality. A mobile video encoding application here is able to achieve the lowest power consumption at any bitrate demand by dynamically switching between functional-unit architectures.

Processors have traditionally been designed for the worst-case, resulting in designs that have high yields, but are expensive in terms of area and power. Better-than-worst-case (BTWC) design approaches based on timing speculation (TS) have recently gained ground as an alternative to traditional designs by allowing processors to be designed for the average case and still maintain high yields.

Current processor designs have a critical operating point that sets a hard limit on voltage scaling. Any scaling beyond the critical voltage results in exceeding the maximum allowable error rate, i.e., there are more timing errors than can be effectively and gainfully detected or corrected by an error-tolerance mechanism. This limits the effectiveness of voltage scaling as a knob for reliability/power tradeoffs. In this paper, we present power-aware slack redistribution, a novel design-level approach to allow voltage/reliability tradeoffs in processors. Techniques based on power-aware slack redistribution reapportion timing slack of the frequently-occurring, near-critical timing paths of a processor in a power- and area-efficient manner, such that we increase the range of voltages over which the incidence of operational (timing) errors is acceptable. This results in soft architectures — designs that fail gracefully, allowing us to perform reliability/power tradeoffs by reducing voltage up to the point that produces maximum allowable errors for our application. The goal of our optimization is to minimize the voltage at which a soft architecture encounters the maximum allowable error rate, thus maximizing the range over which voltage scaling is possible and minimizing power consumption for a given error rate. Our experiments demonstrate 23% power savings over the baseline design at an error rate of 1%. Observed power reductions are 29%, 29%, 19%, and 20% for error rates of 2%, 4%, 8%, and 16% respectively. Benefits are higher in the face of error recovery using Razor. Area overhead of our techniques is up to 2.7%.

Timing speculation has been proposed as a technique for maximizing the energy efficiency of processors with minimal loss in performance. A typical implementation of timing speculation involves speculatively reducing the voltage of a processor to a point where errors are possible but rare, and employing an error recovery mechanism to ensure correct functionality. This allows significant energy savings with a small recovery overhead. Previous work on timing speculation has either explored the benefits of customizing the design methodology for a particular error resilience mechanism or has attempted to understand the benefits from error resilience for a particular processor design. There is no work, to the best of our knowledge, that attempts to understand the benefits of co-optimizing microarchitecture and error resilience. In this paper, we present the first study on co-optimizing a processor pipeline and an error resilience mechanism. We develop an analytical model that relates the benefits from error resiliency to the depth of the pipeline as well as its circuit structure. The model is then used to determine the optimal pipeline depth for different energy efficiency metrics for different error resilience overheads. Our results demonstrate that several interesting relationships exist between error resilience and pipeline structure. For example, we show that there are significant energy efficiency benefits to pipelining an architecture for an error resiliency mechanism vs error resiliency-agnostic pipelining. As another example, we show that benefits from error resiliency are greater for short pipelines than long pipelines. We also confirm that the benefits from error resiliency are higher when the circuit structure is such that error rate increases slowly on reducing input voltage vs a circuit optimized for power where a slack wall exists at the nominal operating point. Finally, we quantify the difference in benefits from error resiliency for irregular vs regular workloads and show that benefits from error resiliency are higher for irregular workloads. Our analytical results were validated using a cycle-accurate simulation-based model.

Recently proposed techniques for peak power management involve centralized decision-making and assume quick evaluation of the various power management states. These techniques do not prevent instantaneous power from exceeding the peak power budget, but instead trigger corrective action when the budget has been exceeded. Similarly, they are not suitable for many-core architectures (processors with tens or possibly hundreds of cores on the same die) due to an exponential explosion in the number of global power management states. In this paper, we look at a hierarchical and a gradient ascent-based technique for decentralized peak power management for many-core architectures. The proposed techniques prevent power from exceeding the peak power budget and enable the placement of several more cores on a die than what the power budget would normally allow. We show up to 47% (33% on average) improvements in throughput for a given power budget. Our techniques outperform the static oracle by 22%.

Recently proposed techniques for peak power management involve centralized decision-making and assume quick evaluation of the various power management states. These techniques suffer from two limitations. First, they do not prevent instantaneous power from exceeding the peak power budget, but instead trigger corrective action when the budget has been exceeded. Second, while these techniques may work for multi-core architectures (processors with small number of cores), they are not suitable for many-core architectures (processors with tens or possibly hundreds of cores on the same die) due to an exponential explosion in the number of global power management states. In this paper, we look at three scalable techniques for peak power management for many-core architectures. The proposed techniques (mapping the power management problem to a knapsack problem, mapping it to a genetic search problem, and mapping it to a simple learning problem with confidence counters) prevent power from exceeding the peak power budget and enable the placement of several more cores on a die than what the power budget would normally allow. We show up to 47% (33% on average) improvements in throughput for a given power budget. Our techniques outperform the static oracle by 22%.