Cross-Layer Automated Hardware Design for Accuracy-Configurable Approximate Computing

Approximate Computing trades off computation accuracy against performance or energy efficiency. It is a design paradigm that arose in the last decade as an answer to diminishing returns from Dennard\u27s scaling and a shift in the prominent workloads. A range of modern workloads, categorized mainly as recognition, mining, and synthesis, features an inherent tolerance to approximations. Their characteristics, such as redundancies in their input data and robust-to-noise algorithms, allow them to produce outputs of acceptable quality, despite an approximation in some of their computations. Approximate Computing leverages the application tolerance by relaxing the exactness in computation towards primary design goals of increasing performance or improving energy efficiency. Existing techniques span across the abstraction layers of computer systems where cross-layer techniques are shown to offer a larger design space and yield higher savings. Currently, the majority of the existing work aims at meeting a single accuracy. The extent of approximation tolerance, however, significantly varies with a change in input characteristics and applications. In this dissertation, methods and implementations are presented for cross-layer and automated design of accuracy-configurable Approximate Computing to maximally exploit the performance and energy benefits. In particular, this dissertation addresses the following challenges and introduces novel contributions: A main Approximate Computing category in hardware is to scale either voltage or frequency beyond the safe limits for power or performance benefits, respectively. The rationale is that timing errors would be gradual and for an initial range tolerable. This scaling enables a fine-grain accuracy-configurability by varying the timing error occurrence. However, conventional synthesis tools aim at meeting a single delay for all paths within the circuit. Subsequently, with voltage or frequency scaling, either all paths succeed, or a large number of paths fail simultaneously, with a steep increase in error rate and magnitude. This dissertation presents an automated method for minimizing path delays by individually constraining the primary outputs of combinational circuits. As a result, it reduces the number of failing paths and makes the timing errors significantly more gradual, and also rarer and smaller on average. Additionally, it reveals that delays can be significantly reduced towards the least significant bit (LSB) and allows operating at a higher frequency when small operands are computed. Precision scaling, i.e., reducing the representation of data and its accuracy is widely used in multiple abstraction layers in Approximate Computing. Reducing data precision also reduces the transistor toggles, and therefore the dynamic power consumption. Application and architecture level precision scaling results in using only LSBs of the circuit. Arithmetic circuits often have less complexity and logic depth in LSBs compared to most significant bits (MSB). To take advantage of this circuit property, a delay-altering synthesis methodology is proposed. The method finds energy-optimal delay values under configurable precision usage and assigns them to primary outputs used for different precisions. Thereby, it enables dynamic frequency-precision scalable circuits for energy efficiency. Within the hardware architecture, it is possible to instantiate multiple units with the same functionality with different fixed approximation levels, where each block benefits from having fewer transistors and also synthesis relaxations. These blocks can be selected dynamically and thus allow to configure the accuracy during runtime. Instantiating such approximate blocks can be a lower dynamic power but higher area and leakage cost alternative to the current state-of-the-art gating mechanisms which switch off a group of paths in the circuit to reduce the toggling activity. Jointly, instantiating multiple blocks and gating mechanisms produce a large design space of accuracy-configurable hardware, where energy-optimal solutions require a cross-layer search in architecture and circuit levels. To that end, an approximate hardware synthesis methodology is proposed with joint optimizations in architecture and circuit for dynamic accuracy scaling, and thereby it enables energy vs. area trade-offs

An Ultra-Low-Power 75mV 64-Bit Current-Mode Majority-Function Adder

Ultra-low-power circuits are becoming more desirable due to growing portable device markets and they are also becoming more interesting and applicable today in biomedical, pharmacy and sensor networking applications because of the nano-metric scaling and CMOS reliability improvements. In this thesis, three main achievements are presented in ultra-low-power adders. First, a new majority function algorithm for carry and the sum generation is presented. Then with this algorithm and implied new architecture, we achieved a circuit with 75mV supply voltage operation. Last but not least, a 64 bit current-mode majority-function adder based on the new architecture and algorithm is successfully tested at 75mV supply voltage. The circuit consumed 4.5nW or 3.8pJ in one of the worst conditions

IMPLEMENTATION OF LOW POWER DELAY PULSED LATCHES FOR SHIFT REGISTER USING KOGGE STONE ADDER

This project proposes delay efficient architecture for shift registers using pulsed latches instead of filp flops. Area and power can be reduced greatly by using latches then flip flops. The timing problem exhibited by the latches is reduced by taking necessary delays in pulses for latches. This includes a counter for generating pulses with delays. For obtaining these delays counter has to incremented by 1. The proposed kogge stone architecture reduces the delay to maximum extent, and produces numerious variation between conventional adder architecture

VLSI Circuits for Approximate Computing

Approximate Computing has recently emerged as a promising solution to enhance circuits performance by relaxing the requisite on exact calculations. Multimedia and Machine Learning constitute a typical example of error resilient, albeit compute-intensive, applications. In this dissertation, the design and optimization of approximate fundamental VLSI digital blocks is investigated. In chapter one the theoretical motivations of Approximate Computing, from the VLSI perspective, are discussed. In chapter two my research activity about approximate adders is reported. In this chapter approximate adders for both traditional non-error tolerant applications and error resilient applications are discussed. In chapter three precision-scalable units are investigated. Real-time precision scalability allows adapting the precision level of the unit with the precision requirements of the applications. In this context my research activities regarding approximate Multiply-and-Accumulate and memory units are described. In chapter four a precision-scalable approximate convolver for computer vision applications is discussed. This is composed of both the approximate Multiply-and-Accumulate and memory units, presented in the chapter three

Designing energy-efficient computing systems using equalization and machine learning

As technology scaling slows down in the nanometer CMOS regime and mobile computing becomes more ubiquitous, designing energy-efficient hardware for mobile systems is becoming increasingly critical and challenging. Although various approaches like near-threshold computing (NTC), aggressive voltage scaling with shadow latches, etc. have been proposed to get the most out of limited battery life, there is still no “silver bullet” to increasing power-performance demands of the mobile systems. Moreover, given that a mobile system could operate in a variety of environmental conditions, like different temperatures, have varying performance requirements, etc., there is a growing need for designing tunable/reconfigurable systems in order to achieve energy-efficient operation. In this work we propose to address the energy- efficiency problem of mobile systems using two different approaches: circuit tunability and distributed adaptive algorithms. Inspired by the communication systems, we developed feedback equalization based digital logic that changes the threshold of its gates based on the input pattern. We showed that feedback equalization in static complementary CMOS logic enabled up to 20% reduction in energy dissipation while maintaining the performance metrics. We also achieved 30% reduction in energy dissipation for pass-transistor digital logic (PTL) with equalization while maintaining performance. In addition, we proposed a mechanism that leverages feedback equalization techniques to achieve near optimal operation of static complementary CMOS logic blocks over the entire voltage range from near threshold supply voltage to nominal supply voltage. Using energy-delay product (EDP) as a metric we analyzed the use of the feedback equalizer as part of various sequential computational blocks. Our analysis shows that for near-threshold voltage operation, when equalization was used, we can improve the operating frequency by up to 30%, while the energy increase was less than 15%, with an overall EDP reduction of ≈10%. We also observe an EDP reduction of close to 5% across entire above-threshold voltage range. On the distributed adaptive algorithm front, we explored energy-efficient hardware implementation of machine learning algorithms. We proposed an adaptive classifier that leverages the wide variability in data complexity to enable energy-efficient data classification operations for mobile systems. Our approach takes advantage of varying classification hardness across data to dynamically allocate resources and improve energy efficiency. On average, our adaptive classifier is ≈100× more energy efficient but has ≈1% higher error rate than a complex radial basis function classifier and is ≈10× less energy efficient but has ≈40% lower error rate than a simple linear classifier across a wide range of classification data sets. We also developed a field of groves (FoG) implementation of random forests (RF) that achieves an accuracy comparable to Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) under tight energy budgets. The FoG architecture takes advantage of the fact that in random forests a small portion of the weak classifiers (decision trees) might be sufficient to achieve high statistical performance. By dividing the random forest into smaller forests (Groves), and conditionally executing the rest of the forest, FoG is able to achieve much higher energy efficiency levels for comparable error rates. We also take advantage of the distributed nature of the FoG to achieve high level of parallelism. Our evaluation shows that at maximum achievable accuracies FoG consumes ≈1.48×, ≈24×, ≈2.5×, and ≈34.7× lower energy per classification compared to conventional RF, SVM-RBF , Multi-Layer Perceptron Network (MLP), and CNN, respectively. FoG is 6.5× less energy efficient than SVM-LR, but achieves 18% higher accuracy on average across all considered datasets

Null convention logic circuits for asynchronous computer architecture

For most of its history, computer architecture has been able to benefit from a rapid scaling in semiconductor technology, resulting in continuous improvements to CPU design. During that period, synchronous logic has dominated because of its inherent ease of design and abundant tools. However, with the scaling of semiconductor processes into deep sub-micron and then to nano-scale dimensions, computer architecture is hitting a number of roadblocks such as high power and increased process variability. Asynchronous techniques can potentially offer many advantages compared to conventional synchronous design, including average case vs. worse case performance, robustness in the face of process and operating point variability and the ready availability of high performance, fine grained pipeline architectures. Of the many alternative approaches to asynchronous design, Null Convention Logic (NCL) has the advantage that its quasi delay-insensitive behavior makes it relatively easy to set up complex circuits without the need for exhaustive timing analysis. This thesis examines the characteristics of an NCL based asynchronous RISC-V CPU and analyses the problems with applying NCL to CPU design. While a number of university and industry groups have previously developed small 8-bit microprocessor architectures using NCL techniques, it is still unclear whether these offer any real advantages over conventional synchronous design. A key objective of this work has been to analyse the impact of larger word widths and more complex architectures on NCL CPU implementations. The research commenced by re-evaluating existing techniques for implementing NCL on programmable devices such as FPGAs. The little work that has been undertaken previously on FPGA implementations of asynchronous logic has been inconclusive and seems to indicate that asynchronous systems cannot be easily implemented in these devices. However, most of this work related to an alternative technique called bundled data, which is not well suited to FPGA implementation because of the difficulty in controlling and matching delays in a 'bundle' of signals. On the other hand, this thesis clearly shows that such applications are not only possible with NCL, but there are some distinct advantages in being able to prototype complex asynchronous systems in a field-programmable technology such as the FPGA. A large part of the value of NCL derives from its architectural level behavior, inherent pipelining, and optimization opportunities such as the merging of register and combina- tional logic functions. In this work, a number of NCL multiplier architectures have been analyzed to reveal the performance trade-offs between various non-pipelined, 1D and 2D organizations. Two-dimensional pipelining can easily be applied to regular architectures such as array multipliers in a way that is both high performance and area-efficient. It was found that the performance of 2D pipelining for small networks such as multipliers is around 260% faster than the equivalent non-pipelined design. However, the design uses 265% more transistors so the methodology is mainly of benefit where performance is strongly favored over area. A pipelined 32bit x 32bit signed Baugh-Wooley multiplier with Wallace-Tree Carry Save Adders (CSA), which is representative of a real design used for CPUs and DSPs, was used to further explore this concept as it is faster and has fewer pipeline stages compared to the normal array multiplier using Ripple-Carry adders (RCA). It was found that 1D pipelining with ripple-carry chains is an efficient implementation option but becomes less so for larger multipliers, due to the completion logic for which the delay time depends largely on the number of bits involved in the completion network. The average-case performance of ripple-carry adders was explored using random input vectors and it was observed that it offers little advantage on the smaller multiplier blocks, but this particular timing characteristic of asynchronous design styles be- comes increasingly more important as word size grows. Finally, this research has resulted in the development of the first 32-Bit asynchronous RISC-V CPU core. Called the Redback RISC, the architecture is a structure of pipeline rings composed of computational oscillations linked with flow completeness relationships. It has been written using NELL, a commercial description/synthesis tool that outputs standard Verilog. The Redback has been analysed and compared to two approximately equivalent industry standard 32-Bit synchronous RISC-V cores (PicoRV32 and Rocket) that are already fabricated and used in industry. While the NCL implementation is larger than both commercial cores it has similar performance and lower power compared to the PicoRV32. The implementation results were also compared against an existing NCL design tool flow (UNCLE), which showed how much the results of these implementation strategies differ. The Redback RISC has achieved similar level of throughput and 43% better power and 34% better energy compared to one of the synchronous cores with the same benchmark test and test condition such as input sup- ply voltage. However, it was shown that area is the biggest drawback for NCL CPU design. The core is roughly 2.5× larger than synchronous designs. On the other hand its area is still 2.9× smaller than previous designs using UNCLE tools. The area penalty is largely due to the unavoidable translation into a dual-rail topology when using the standard NCL cell library

Approximate computing: An integrated cross-layer framework

A new design approach, called approximate computing (AxC), leverages the flexibility provided by intrinsic application resilience to realize hardware or software implementations that are more efficient in energy or performance. Approximate computing techniques forsake exact (numerical or Boolean) equivalence in the execution of some of the application’s computations, while ensuring that the output quality is acceptable. While early efforts in approximate computing have demonstrated great potential, they consist of ad hoc techniques applied to a very narrow set of applications, leaving in question the applicability of approximate computing in a broader context. The primary objective of this thesis is to develop an integrated cross-layer approach to approximate computing, and to thereby establish its applicability to a broader range of applications. The proposed framework comprises of three key components: (i) At the circuit level, systematic approaches to design approximate circuits, or circuits that realize a slightly modified function with improved efficiency, (ii) At the architecture level, utilize approximate circuits to build programmable approximate processors, and (iii) At the software level, methods to apply approximate computing to machine learning classifiers, which represent an important class of applications that are being utilized across the computing spectrum. Towards this end, the thesis extends the state-of-the-art in approximate computing in the following important directions. Synthesis of Approximate Circuits: First, the thesis proposes a rigorous framework for the automatic synthesis of approximate circuits , which are the hardware building blocks of approximate computing platforms. Designing approximate circuits involves making judicious changes to the function implemented by the circuit such that its hardware complexity is lowered without violating the specified quality constraint. Inspired by classical approaches to Boolean optimization in logic synthesis, the thesis proposes two synthesis tools called SALSA and SASIMI that are general, i.e., applicable to any given circuit and quality specification. The framework is further extended to automatically design quality configurable circuits , which are approximate circuits with the capability to reconfigure their quality at runtime. Over a wide range of arithmetic circuits, complex modules and complete datapaths, the circuits synthesized using the proposed framework demonstrate significant benefits in area and energy. Programmable AxC Processors: Next, the thesis extends approximate computing to the realm of programmable processors by introducing the concept of quality programmable processors (QPPs). A key principle of QPPs is that the notion of quality is explicitly codified in their HW/SW interface i.e., the instruction set. Instructions in the ISA are extended with quality fields, enabling software to specify the accuracy level that must be met during their execution. The micro-architecture is designed with hardware mechanisms to understand these quality specifications and translate them into energy savings. As a first embodiment of QPPs, the thesis presents a quality programmable 1D/2D vector processor QP-Vec, which contains a 3-tiered hierarchy of processing elements. Based on an implementation of QP-Vec with 289 processing elements, energy benefits up to 2.5X are demonstrated across a wide range of applications. Software and Algorithms for AxC: Finally, the thesis addresses the problem of applying approximate computing to an important class of applications viz. machine learning classifiers such as deep learning networks. To this end, the thesis proposes two approaches—AxNN and scalable effort classifiers. Both approaches leverage domain- specific insights to transform a given application to an energy-efficient approximate version that meets a specified application output quality. In the context of deep learning networks, AxNN adapts backpropagation to identify neurons that contribute less significantly to the network’s accuracy, approximating these neurons (e.g., by using lower precision), and incrementally re-training the network to mitigate the impact of approximations on output quality. On the other hand, scalable effort classifiers leverage the heterogeneity in the inherent classification difficulty of inputs to dynamically modulate the effort expended by machine learning classifiers. This is achieved by building a chain of classifiers of progressively growing complexity (and accuracy) such that the number of stages used for classification scale with input difficulty. Scalable effort classifiers yield substantial energy benefits as a majority of the inputs require very low effort in real-world datasets. In summary, the concepts and techniques presented in this thesis broaden the applicability of approximate computing, thus taking a significant step towards bringing approximate computing to the mainstream. (Abstract shortened by ProQuest.

Characterization and mitigation of process variation in digital circuits and systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 155-166).Process variation threatens to negate a whole generation of scaling in advanced process technologies due to performance and power spreads of greater than 30-50%. Mitigating this impact requires a thorough understanding of the variation sources, magnitudes and spatial components at the device, circuit and architectural levels. This thesis explores the impacts of variation at each of these levels and evaluates techniques to alleviate them in the context of digital circuits and systems. At the device level, we propose isolation and measurement of variation in the intrinsic threshold voltage of a MOSFET using sub-threshold leakage currents. Analysis of the measured data, from a test-chip implemented on a 0. 18[mu]m CMOS process, indicates that variation in MOSFET threshold voltage is a truly random process dependent only on device dimensions. Further decomposition of the observed variation reveals no systematic within-die variation components nor any spatial correlation. A second test-chip capable of characterizing spatial variation in digital circuits is developed and implemented in a 90nm triple-well CMOS process. Measured variation results show that the within-die component of variation is small at high voltages but is an increasing fraction of the total variation as power-supply voltage decreases. Once again, the data shows no evidence of within-die spatial correlation and only weak systematic components. Evaluation of adaptive body-biasing and voltage scaling as variation mitigation techniques proves voltage scaling is more effective in performance modification with reduced impact to idle power compared to body-biasing.(cont.) Finally, the addition of power-supply voltages in a massively parallel multicore processor is explored to reduce the energy required to cope with process variation. An analytic optimization framework is developed and analyzed; using a custom simulation methodology, total energy of a hypothetical 1K-core processor based on the RAW core is reduced by 6-16% with the addition of only a single voltage. Analysis of yield versus required energy demonstrates that a combination of disabling poor-performing cores and additional power-supply voltages results in an optimal trade-off between performance and energy.by Nigel Anthony Drego.Ph.D

Low energy digital circuit design using sub-threshold operation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2006.Includes bibliographical references (p. 189-202).Scaling of process technologies to deep sub-micron dimensions has made power management a significant concern for circuit designers. For emerging low power applications such as distributed micro-sensor networks or medical applications, low energy operation is the primary concern instead of speed, with the eventual goal of harvesting energy from the environment. Sub-threshold operation offers a promising solution for ultra-low-energy applications because it often achieves the minimum energy per operation. While initial explorations into sub-threshold circuits demonstrate its promise, sub-threshold circuit design remains in its infancy. This thesis makes several contributions that make sub-threshold design more accessible to circuit designers. First, a model for energy consumption in sub-threshold provides an analytical solution for the optimum VDD to minimize energy. Fitting this model to a generic circuit allows easy estimation of the impact of processing and environmental parameters on the minimum energy point. Second, analysis of device sizing for sub-threshold circuits shows the trade-offs between sizing for minimum energy and for minimum voltage operation.(cont.) A programmable FIR filter test chip fabricated in 0.18pum bulk CMOS provides measurements to confirm the model and the sizing analysis. Third, a low-overhead method for integrating sub-threshold operation with high performance applications extends dynamic voltage scaling across orders of magnitude of frequency and provides energy scalability down to the minimum energy point. A 90nm bulk CMOS test chip confirms the range of operation for ultra-dynamic voltage scaling. Finally, sub-threshold operation is extended to memories. Analysis of traditional SRAM bitcells and architectures leads to development of a new bitcell for robust sub-threshold SRAM operation. The sub-threshold SRAM is analyzed experimentally in a 65nm bulk CMOS test chip.by Benton H. Calhoun.Ph.D

Timing speculation and adaptive reliable overclocking techniques for aggressive computer systems

Computers have changed our lives beyond our own imagination in the past several decades. The continued and progressive advancements in VLSI technology and numerous micro-architectural innovations have played a key role in the design of spectacular low-cost high performance computing systems that have become omnipresent in today\u27s technology driven world. Performance and dependability have become key concerns as these ubiquitous computing machines continue to drive our everyday life. Every application has unique demands, as they run in diverse operating environments. Dependable, aggressive and adaptive systems improve efficiency in terms of speed, reliability and energy consumption. Traditional computing systems run at a fixed clock frequency, which is determined by taking into account the worst-case timing paths, operating conditions, and process variations. Timing speculation based reliable overclocking advocates going beyond worst-case limits to achieve best performance while not avoiding, but detecting and correcting a modest number of timing errors. The success of this design methodology relies on the fact that timing critical paths are rarely exercised in a design, and typical execution happens much faster than the timing requirements dictated by worst-case design methodology. Better-than-worst-case design methodology is advocated by several recent research pursuits, which exploit dependability techniques to enhance computer system performance. In this dissertation, we address different aspects of timing speculation based adaptive reliable overclocking schemes, and evaluate their role in the design of low-cost, high performance, energy efficient and dependable systems. We visualize various control knobs in the design that can be favorably controlled to ensure different design targets. As part of this research, we extend the SPRIT3E, or Superscalar PeRformance Improvement Through Tolerating Timing Errors, framework, and characterize the extent of application dependent performance acceleration achievable in superscalar processors by scrutinizing the various parameters that impact the operation beyond worst-case limits. We study the limitations imposed by short-path constraints on our technique, and present ways to exploit them to maximize performance gains. We analyze the sensitivity of our technique\u27s adaptiveness by exploring the necessary hardware requirements for dynamic overclocking schemes. Experimental analysis based on SPEC2000 benchmarks running on a SimpleScalar Alpha processor simulator, augmented with error rate data obtained from hardware simulations of a superscalar processor, are presented. Even though reliable overclocking guarantees functional correctness, it leads to higher power consumption. As a consequence, reliable overclocking without considering on-chip temperatures will bring down the lifetime reliability of the chip. In this thesis, we analyze how reliable overclocking impacts the on-chip temperature of a microprocessor and evaluate the effects of overheating, due to such reliable dynamic frequency tuning mechanisms, on the lifetime reliability of these systems. We then evaluate the effect of performing thermal throttling, a technique that clamps the on-chip temperature below a predefined value, on system performance and reliability. Our study shows that a reliably overclocked system with dynamic thermal management achieves 25% performance improvement, while lasting for 14 years when being operated within 353K. Over the past five decades, technology scaling, as predicted by Moore\u27s law, has been the bedrock of semiconductor technology evolution. The continued downscaling of CMOS technology to deep sub-micron gate lengths has been the primary reason for its dominance in today\u27s omnipresent silicon microchips. Even as the transition to the next technology node is indispensable, the initial cost and time associated in doing so presents a non-level playing field for the competitors in the semiconductor business. As part of this thesis, we evaluate the capability of speculative reliable overclocking mechanisms to maximize performance at a given technology level. We evaluate its competitiveness when compared to technology scaling, in terms of performance, power consumption, energy and energy delay product. We present a comprehensive comparison for integer and floating point SPEC2000 benchmarks running on a simulated Alpha processor at three different technology nodes in normal and enhanced modes. Our results suggest that adopting reliable overclocking strategies will help skip a technology node altogether, or be competitive in the market, while porting to the next technology node. Reliability has become a serious concern as systems embrace nanometer technologies. In this dissertation, we propose a novel fault tolerant aggressive system that combines soft error protection and timing error tolerance. We replicate both the pipeline registers and the pipeline stage combinational logic. The replicated logic receives its inputs from the primary pipeline registers while writing its output to the replicated pipeline registers. The organization of redundancy in the proposed Conjoined Pipeline system supports overclocking, provides concurrent error detection and recovery capability for soft errors, intermittent faults and timing errors, and flags permanent silicon defects. The fast recovery process requires no checkpointing and takes three cycles. Back annotated post-layout gate-level timing simulations, using 45nm technology, of a conjoined two-stage arithmetic pipeline and a conjoined five-stage DLX pipeline processor, with forwarding logic, show that our approach, even under a severe fault injection campaign, achieves near 100% fault coverage and an average performance improvement of about 20%, when dynamically overclocked