Design Techniques for Energy-Quality Scalable Digital Systems

Energy efficiency is one of the key design goals in modern computing. Increasingly complex tasks are being executed in mobile devices and Internet of Things end-nodes, which are expected to operate for long time intervals, in the orders of months or years, with the limited energy budgets provided by small form-factor batteries. Fortunately, many of such tasks are error resilient, meaning that they can toler- ate some relaxation in the accuracy, precision or reliability of internal operations, without a significant impact on the overall output quality. The error resilience of an application may derive from a number of factors. The processing of analog sensor inputs measuring quantities from the physical world may not always require maximum precision, as the amount of information that can be extracted is limited by the presence of external noise. Outputs destined for human consumption may also contain small or occasional errors, thanks to the limited capabilities of our vision and hearing systems. Finally, some computational patterns commonly found in domains such as statistics, machine learning and operational research, naturally tend to reduce or eliminate errors. Energy-Quality (EQ) scalable digital systems systematically trade off the quality of computations with energy efficiency, by relaxing the precision, the accuracy, or the reliability of internal software and hardware components in exchange for energy reductions. This design paradigm is believed to offer one of the most promising solutions to the impelling need for low-energy computing. Despite these high expectations, the current state-of-the-art in EQ scalable design suffers from important shortcomings. First, the great majority of techniques proposed in literature focus only on processing hardware and software components. Nonetheless, for many real devices, processing contributes only to a small portion of the total energy consumption, which is dominated by other components (e.g. I/O, memory or data transfers). Second, in order to fulfill its promises and become diffused in commercial devices, EQ scalable design needs to achieve industrial level maturity. This involves moving from purely academic research based on high-level models and theoretical assumptions to engineered flows compatible with existing industry standards. Third, the time-varying nature of error tolerance, both among different applications and within a single task, should become more central in the proposed design methods. This involves designing “dynamic” systems in which the precision or reliability of operations (and consequently their energy consumption) can be dynamically tuned at runtime, rather than “static” solutions, in which the output quality is fixed at design-time. This thesis introduces several new EQ scalable design techniques for digital systems that take the previous observations into account. Besides processing, the proposed methods apply the principles of EQ scalable design also to interconnects and peripherals, which are often relevant contributors to the total energy in sensor nodes and mobile systems respectively. Regardless of the target component, the presented techniques pay special attention to the accurate evaluation of benefits and overheads deriving from EQ scalability, using industrial-level models, and on the integration with existing standard tools and protocols. Moreover, all the works presented in this thesis allow the dynamic reconfiguration of output quality and energy consumption. More specifically, the contribution of this thesis is divided in three parts. In a first body of work, the design of EQ scalable modules for processing hardware data paths is considered. Three design flows are presented, targeting different technologies and exploiting different ways to achieve EQ scalability, i.e. timing-induced errors and precision reduction. These works are inspired by previous approaches from the literature, namely Reduced-Precision Redundancy and Dynamic Accuracy Scaling, which are re-thought to make them compatible with standard Electronic Design Automation (EDA) tools and flows, providing solutions to overcome their main limitations. The second part of the thesis investigates the application of EQ scalable design to serial interconnects, which are the de facto standard for data exchanges between processing hardware and sensors. In this context, two novel bus encodings are proposed, called Approximate Differential Encoding and Serial-T0, that exploit the statistical characteristics of data produced by sensors to reduce the energy consumption on the bus at the cost of controlled data approximations. The two techniques achieve different results for data of different origins, but share the common features of allowing runtime reconfiguration of the allowed error and being compatible with standard serial bus protocols. Finally, the last part of the manuscript is devoted to the application of EQ scalable design principles to displays, which are often among the most energy- hungry components in mobile systems. The two proposals in this context leverage the emissive nature of Organic Light-Emitting Diode (OLED) displays to save energy by altering the displayed image, thus inducing an output quality reduction that depends on the amount of such alteration. The first technique implements an image-adaptive form of brightness scaling, whose outputs are optimized in terms of balance between power consumption and similarity with the input. The second approach achieves concurrent power reduction and image enhancement, by means of an adaptive polynomial transformation. Both solutions focus on minimizing the overheads associated with a real-time implementation of the transformations in software or hardware, so that these do not offset the savings in the display. For each of these three topics, results show that the aforementioned goal of building EQ scalable systems compatible with existing best practices and mature for being integrated in commercial devices can be effectively achieved. Moreover, they also show that very simple and similar principles can be applied to design EQ scalable versions of different system components (processing, peripherals and I/O), and to equip these components with knobs for the runtime reconfiguration of the energy versus quality tradeoff

근사 컴퓨팅을 이용한 회로 노화 보상과 에너지 효율적인 신경망 구현

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 이혁재.Approximate computing reduces the cost (energy and/or latency) of computations by relaxing the correctness (i.e., precision) of computations up to the level, which is dependent on types of applications. Moreover, it can be realized in various hierarchies of computing system design from circuit level to application level. This dissertation presents the methodologies applying approximate computing across such hierarchies; compensating aging-induced delay in logic circuit by dynamic computation approximation (Chapter 1), designing energy-efficient neural network by combining low-power and low-latency approximate neuron models (Chapter 2), and co-designing in-memory gradient descent module with neural processing unit so as to address a memory bottleneck incurred by memory I/O for high-precision data (Chapter 3). The first chapter of this dissertation presents a novel design methodology to turn the timing violation caused by aging into computation approximation error without the reliability guardband or increasing the supply voltage. It can be realized by accurately monitoring the critical path delay at run-time. The proposal is evaluated at two levels: RTL component level and system level. The experimental results at the RTL component level show a significant improvement in terms of (normalized) mean squared error caused by the timing violation and, at the system level, show that the proposed approach successfully transforms the aging-induced timing violation errors into much less harmful computation approximation errors, therefore it recovers image quality up to perceptually acceptable levels. It reduces the dynamic and static power consumption by 21.45% and 10.78%, respectively, with 0.8% area overhead compared to the conventional approach. The second chapter of this dissertation presents an energy-efficient neural network consisting of alternative neuron models; Stochastic-Computing (SC) and Spiking (SP) neuron models. SC has been adopted in various fields to improve the power efficiency of systems by performing arithmetic computations stochastically, which approximates binary computation in conventional computing systems. Moreover, a recent work showed that deep neural network (DNN) can be implemented in the manner of stochastic computing and it greatly reduces power consumption. However, Stochastic DNN (SC-DNN) suffers from problem of high latency as it processes only a bit per cycle. To address such problem, it is proposed to adopt Spiking DNN (SP-DNN) as an input interface for SC-DNN since SP effectively processes more bits per cycle than SC-DNN. Moreover, this chapter resolves the encoding mismatch problem, between two different neuron models, without hardware cost by compensating the encoding mismatch with synapse weight calibration. A resultant hybrid DNN (SPSC-DNN) consists of SP-DNN as bottom layers and SC-DNN as top layers. Exploiting the reduced latency from SP-DNN and low-power consumption from SC-DNN, the proposed SPSC-DNN achieves improved energy-efficiency with lower error-rate compared to SC-DNN and SP-DNN in same network configuration. The third chapter of this dissertation proposes GradPim architecture, which accelerates the parameter updates by in-memory processing which is codesigned with 8-bit floating-point training in Neural Processing Unit (NPU) for deep neural networks. By keeping the high precision processing algorithms in memory, such as the parameter update incorporating high-precision weights in its computation, the GradPim architecture can achieve high computational efficiency using 8-bit floating point in NPU and also gain power efficiency by eliminating massive high-precision data transfers between NPU and off-chip memory. A simple extension of DDR4 SDRAM utilizing bank-group parallelism makes the operation designs in processing-in-memory (PIM) module efficient in terms of hardware cost and performance. The experimental results show that the proposed architecture can improve the performance of the parameter update phase in the training by up to 40% and greatly reduce the memory bandwidth requirement while posing only a minimal amount of overhead to the protocol and the DRAM area.근사 컴퓨팅은 연산의 정확도의 손실을 어플리케이션 별 적절한 수준까지 허용함으로써 연산에 필요한 비용 (에너지나 지연시간)을 줄인다. 게다가, 근사 컴퓨팅은 컴퓨팅 시스템 설계의 회로 계층부터 어플리케이션 계층까지 다양한 계층에 적용될 수 있다. 본 논문에서는 근사 컴퓨팅 방법론을 다양한 시스템 설계의 계층에 적용하여 전력과 에너지 측면에서 이득을 얻을 수 있는 방법들을 제안하였다. 이는, 연산 근사화 (computation Approximation)를 통해 회로의 노화로 인해 증가된 지연시간을 추가적인 전력소모 없이 보상하는 방법과 (챕터 1), 근사 뉴런모델 (approximate neuron model)을 이용해 에너지 효율이 높은 신경망을 구성하는 방법 (챕터 2), 그리고 메모리 대역폭으로 인한 병목현상 문제를 높은 정확도 데이터를 활용한 연산을 메모리 내에서 수행함으로써 완화시키는 방법을 (챕터3) 제안하였다. 첫 번째 챕터는 회로의 노화로 인한 지연시간위반을 (timing violation) 설계마진이나 (reliability guardband) 공급전력의 증가 없이 연산오차 (computation approximation error)를 통해 보상하는 설계방법론 (design methodology)를 제안하였다. 이를 위해 주요경로의 (critical path) 지연시간을 동작시간에 정확하게 측정할 필요가 있다. 여기서 제안하는 방법론은 RTL component와 system 단계에서 평가되었다. RTL component 단계의 실험결과를 통해 제안한 방식이 표준화된 평균제곱오차를 (normalized mean squared error) 상당히 줄였음을 볼 수 있다. 그리고 system 단계에서는 이미지처리 시스템에서 이미지의 품질이 인지적으로 충분히 회복되는 것을 보임으로써 회로노화로 인해 발생한 지연시간위반 오차가 에러의 크기가 작은 연산오차로 변경되는 것을 확인 할 수 있었다. 결론적으로, 제안된 방법론을 따랐을 때 0.8%의 공간을 (area) 더 사용하는 비용을 지불하고 21.45%의 동적전력소모와 (dynamic power consumption) 10.78%의 정적전력소모의 (static power consumption) 감소를 달성할 수 있었다. 두 번째 챕터는 근사 뉴런모델을 활용하는 고-에너지효율의 신경망을 (neural network) 제안하였다. 본 논문에서 사용한 두 가지의 근사 뉴런모델은 확률컴퓨팅과 (stochastic computing) 스파이킹뉴런 (spiking neuron) 이론들을 기반으로 모델링되었다. 확률컴퓨팅은 산술연산들을 확률적으로 수행함으로써 이진연산을 낮은 전력소모로 수행한다. 최근에 확률컴퓨팅 뉴런모델을 이용하여 심층 신경망 (deep neural network)를 구현할 수 있다는 연구가 진행되었다. 그러나, 확률컴퓨팅을 뉴런모델링에 활용할 경우 심층신경망이 매 클락사이클마다 (clock cycle) 하나의 비트만을 (bit) 처리하므로, 지연시간 측면에서 매우 나쁠 수 밖에 없는 문제가 있다. 따라서 본 논문에서는 이러한 문제를 해결하기 위하여 스파이킹 뉴런모델로 구성된 스파이킹 심층신경망을 확률컴퓨팅을 활용한 심층신경망 구조와 결합하였다. 스파이킹 뉴런모델의 경우 매 클락사이클마다 여러 비트를 처리할 수 있으므로 심층신경망의 입력 인터페이스로 사용될 경우 지연시간을 줄일 수 있다. 하지만, 확률컴퓨팅 뉴런모델과 스파이킹 뉴런모델의 경우 부호화 (encoding) 방식이 다른 문제가 있다. 따라서 본 논문에서는 해당 부호화 불일치 문제를 모델의 파라미터를 학습할 때 고려함으로써, 파라미터들의 값이 부호화 불일치를 고려하여 조절 (calibration) 될 수 있도록 하여 문제를 해결하였다. 이러한 분석의 결과로, 앞 쪽에는 스파이킹 심층신경망을 배치하고 뒷 쪽애는 확률컴퓨팅 심층신경망을 배치하는 혼성신경망을 제안하였다. 혼성신경망은 스파이킹 심층신경망을 통해 매 클락사이클마다 처리되는 비트 양의 증가로 인한 지연시간 감소 효과와 확률컴퓨팅 심층신경망의 저전력 소모 특성을 모두 활용함으로써 각 심층신경망을 따로 사용하는 경우 대비 우수한 에너지 효율성을 비슷하거나 더 나은 정확도 결과를 내면서 달성한다. 세 번째 챕터는 심층신경망을 8비트 부동소숫점 연산으로 학습하는 신경망처리유닛의 (neural processing unit) 파라미터 갱신을 (parameter update) 메모리-내-연산으로 (in-memory processing) 가속하는 GradPIM 아키텍쳐를 제안하였다. GradPIM은 8비트의 낮은 정확도 연산은 신경망처리유닛에 남기고, 높은 정확도를 가지는 데이터를 활용하는 연산은 (파라미터 갱신) 메모리 내부에 둠으로써 신경망처리유닛과 메모리간의 데이터통신의 양을 줄여, 높은 연산효율과 전력효율을 달성하였다. 또한, GradPIM은 bank-group 수준의 병렬화를 이루어 내 높은 내부 대역폭을 활용함으로써 메모리 대역폭을 크게 확장시킬 수 있게 되었다. 또한 이러한 메모리 구조의 변경이 최소화되었기 때문에 추가적인 하드웨어 비용도 최소화되었다. 실험 결과를 통해 GradPIM이 최소한의 DRAM 프로토콜 변화와 DRAM칩 내의 공간사용을 통해 심층신경망 학습과정 중 파라미터 갱신에 필요한 시간을 40%만큼 향상시켰음을 보였다.Chapter I: Dynamic Computation Approximation for Aging Compensation 1 1.1 Introduction 1 1.1.1 Chip Reliability 1 1.1.2 Reliability Guardband 2 1.1.3 Approximate Computing in Logic Circuits 2 1.1.4 Computation approximation for Aging Compensation 3 1.1.5 Motivational Case Study 4 1.2 Previous Work 5 1.2.1 Aging-induced Delay 5 1.2.2 Delay-Configurable Circuits 6 1.3 Proposed System 8 1.3.1 Overview of the Proposed System 8 1.3.2 Proposed Adder 9 1.3.3 Proposed Multiplier 11 1.3.4 Proposed Monitoring Circuit 16 1.3.5 Aging Compensation Scheme 19 1.4 Design Methodology 20 1.5 Evaluation 24 1.5.1 Experimental setup 24 1.5.2 RTL component level Adder/Multiplier 27 1.5.3 RTL component level Monitoring circuit 30 1.5.4 System level 31 1.6 Summary 38 Chapter II: Energy-Efficient Neural Network by Combining Approximate Neuron Models 40 2.1 Introduction 40 2.1.1 Deep Neural Network (DNN) 40 2.1.2 Low-power designs for DNN 41 2.1.3 Stochastic-Computing Deep Neural Network 41 2.1.4 Spiking Deep Neural Network 43 2.2 Hybrid of Stochastic and Spiking DNNs 44 2.2.1 Stochastic-Computing vs Spiking Deep Neural Network 44 2.2.2 Combining Spiking Layers and Stochastic Layers 46 2.2.3 Encoding Mismatch 47 2.3 Evaluation 49 2.3.1 Latency and Test Error 49 2.3.2 Energy Efficiency 51 2.4 Summary 54 Chapter III: GradPIM: In-memory Gradient Descent in Mixed-Precision DNN Training 55 3.1 Introduction 55 3.1.1 Neural Processing Unit 55 3.1.2 Mixed-precision Training 56 3.1.3 Mixed-precision Training with In-memory Gradient Descent 57 3.1.4 DNN Parameter Update Algorithms 59 3.1.5 Modern DRAM Architecture 61 3.1.6 Motivation 63 3.2 Previous Work 65 3.2.1 Processing-In-Memory 65 3.2.2 Co-design Neural Processing Unit and Processing-In-Memory 66 3.2.3 Low-precision Computation in NPU 67 3.3 GradPIM 68 3.3.1 GradPIM Architecture 68 3.3.2 GradPIM Operations 69 3.3.3 Timing Considerations 70 3.3.4 Update Phase Procedure 73 3.3.5 Commanding GradPIM 75 3.4 NPU Co-design with GradPIM 76 3.4.1 NPU Architecture 76 3.4.2 Data Placement 79 3.5 Evaluation 82 3.5.1 Evaluation Methodology 82 3.5.2 Experimental Results 83 3.5.3 Sensitivity Analysis 88 3.5.4 Layer Characterizations 90 3.5.5 Distributed Data Parallelism 90 3.6 Summary 92 3.6.1 Discussion 92 Bibliography 113 요약 114Docto

Low-Power and Error-Resilient VLSI Circuits and Systems.

Efficient low-power operation is critically important for the success of the next-generation signal processing applications. Device and supply voltage have been continuously scaled to meet a more constrained power envelope, but scaling has created resiliency challenges, including increasing timing faults and soft errors. Our research aims at designing low-power and robust circuits and systems for signal processing by drawing circuit, architecture, and algorithm approaches. To gain an insight into the system faults due to supply voltage reduction, we researched the two primary effects that determine the minimum supply voltage (VMIN) in Intel’s tri-gate CMOS technology, namely process variations and gate-dielectric soft breakdown. We determined that voltage scaling increases the timing window that sequential circuits are vulnerable. Thus, we proposed a new hold-time violation metric to define hold-time VMIN, which has been adopted as a new design standard. Device scaling increases soft errors which affect circuit reliability. Through extensive soft error characterization using two 65nm CMOS test chips, we studied the soft error mechanisms and its dependence on supply voltage and clock frequency. This study laid the foundation of the first 65nm DSP chip design for a NASA spaceflight project. To mitigate such random errors, we proposed a new confidence-driven architecture that effectively enhances the error resiliency of deeply scaled CMOS and post-CMOS circuits. Designing low-power resilient systems can effectively leverage application-specific algorithmic approaches. To explore design opportunities in the algorithmic domain, we demonstrate an application-specific detection and decoding processor for multiple-input multiple-output (MIMO) wireless communication. To enhance the receive error rate for a robust wireless communication, we designed a joint detection and decoding technique by enclosing detection and decoding in an iterative loop to enhance both interference cancellation and error reduction. A proof-of-concept chip design was fabricated for the next-generation 4x4 256QAM MIMO systems. Through algorithm-architecture optimizations and low-power circuit techniques, our design achieves significant improvements in throughput, energy efficiency and error rate, paving the way for future developments in this area.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110323/1/uchchen_1.pd

Libra: Achieving Efficient Instruction- and Data- Parallel Execution for Mobile Applications.

Mobile computing as exemplified by the smart phone has become an integral part of our daily lives. The next generation of these devices will be driven by providing richer user experiences and compelling capabilities: higher definition multimedia, 3D graphics, augmented reality, and voice interfaces. To meet these goals, the core computing capabilities of the smart phone must be scaled. But, the energy budgets are increasing at a much lower rate, thus fundamental improvements in computing efficiency must be garnered. To meet this challenge, computer architects employ hardware accelerators in the form of SIMD and VLIW. Single-instruction multiple-data (SIMD) accelerators provide high degrees of scalability for applications rich in data-level parallelism (DLP). Very long instruction word (VLIW) accelerators provide moderate scalability for applications with high degrees of instruction-level parallelism (ILP). Unfortunately, applications are not so nicely partitioned into two groups: many applications have some DLP, but also contain significant fractions of code with low trip count loops, complex control/data dependences, or non-uniform execution behavior for which no DLP exists. Therefore, a more adaptive accelerator is required to be able to deploy resources as needed: exploit DLP on SIMD when it’s available, but fall back to ILP on the same hardware when necessary. In this thesis, we first focus on various compiler solutions that solve inefficiency problem in both VLIW and SIMD accelerators. For SIMD accelerators, a new vectorization pass, called SIMD Defragmenter, is introduced to uncover hidden DLP using subgraph identification in SIMD accelerators. CGRA express effectively accelerates sequential code regions using a bypass network in VLIW accelerators, and Resource Recycling leverages stream-graph modulo scheduling technique for scheduling of multiple code regions in multi-core accelerators. Second, we propose the new scalable multicore accelerator referred to as Libra for mobile systems, which can support execution of code regions having both DLP and ILP, as well as hybrid combinations of the two. We believe that as industry requires higher performance, the proposed flexible accelerator and compiler support will put more resources to work in order to meet the performance and power efficiency requirements.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99840/1/yjunpark_1.pd

Χρήση μοντέλου παράλληλου προγραμματισμού για σύνθεση αρχιτεκτονικών

The problem of automatically generating hardware modules from high level application representations has been at the forefront of EDA research during the last few years. In this Dissertation we introduce a methodology to automatically synthesize hardware accelerators from OpenCL applications. OpenCL is a recent industry supported standard for writing programs that execute on multicore platforms and accelerators such as GPUs. Our methodology maps OpenCL kernels into hardware accelerators based on architectural templates that explicitly decouple computation from memory communication whenever this is possible. The templates can be tuned to provide a wide repertoire of accelerators that meet user performance requirements and FPGA device characteristics. Furthermore a set of high- and low-level compiler optimizations is applied to generate optimized accelerators. Our experimental evaluation shows that the generated accelerators are tuned efficiently to match the applications memory access pattern and computational complexity and to achieve user performance requirements. An important objective of our tool is to expand the FPGA development user base to software engineers thereby expanding the scope of FPGAs beyond the realm of hardware design.To πρόβλημα της αυτόματης δημιουργίας μονάδων υλικό από παραστάσεις υψηλού επιπέδου εφαρμογής είναι στην πρώτη γραμμή της EDA έρευνας κατά τη διάρκεια των τελευταίων ετών. Σε αυτή την διατριβή παρουσιάζουμε μια μεθοδολογία για τη αυτόματη σύνθεση επιταχυντές υλικού από εφαρμογές OpenCL. OpenCL είναι ένα πρόσφατο πρότυπο για τη σύνταξη των προγραμμάτων που εκτελούνται σε πλατφόρμες πολλαπλών πυρήνων και επιταχυντές όπως GPUs. Η μεθοδολογία μας μετατρέπει προγράμματα OpenCL σε επιταχυντές υλικού με βάση αρχιτεκτονικά πρότυπα που ρητά αποσυνδέει τους υπολογισμούς από την μεταφορά δεδομένων από/προς την μνήμη όποτε αυτό είναι δυνατό. Τα πρότυπα μπορούν να συντονιστούν ώστε να παρέχουν ένα ευρύ ρεπερτόριο από επιταχυντές που πληρούν τις απαιτήσεις απόδοσης των χρηστών και τα χαρακτηριστικά της συσκευής FPGA. Επιπλέον ένα σύνολο υψηλής και χαμηλής στάθμης βελτιστοποιήσεις μεταγλωττιστή εφαρμόζεται για να παράγει βελτιστοποιημένα επιταχυντές. Η πειραματική αξιολόγηση δείχνει ότι οι επιταχυντές που δημιουργούνται αποτελεσματικά συντονισμένοι για να ταιριάζει με το μοτίβο πρόσβασης στην μνήμη κάθε εφαρμογής και την υπολογιστική πολυπλοκότητα και να επιτύχουν τις απαιτήσεις απόδοσης των χρηστών. Ένας σημαντικός στόχος του εργαλείου μας είναι η επέκταση της βάσης χρηστών πλατφόρμες FPGA για μηχανικούς λογισμικού ώστε να γίνει ανάπτυξη FPGA συστήματα από μηχανικούς λογισμικού χωρίς την ανάγκη για εμπειρία σχεδιασμού υλικού

ULTRA ENERGY-EFFICIENT SUB-/NEAR-THRESHOLD COMPUTING: PLATFORM AND METHODOLOGY

Ph.DDOCTOR OF PHILOSOPH

Architecture and Analysis for Next Generation Mobile Signal Processing.

Mobile devices have proliferated at a spectacular rate, with more than 3.3 billion active cell phones in the world. With sales totaling hundreds of billions every year, the mobile phone has arguably become the dominant computing platform, replacing the personal computer. Soon, improvements to today’s smart phones, such as high-bandwidth internet access, high-definition video processing, and human-centric interfaces that integrate voice recognition and video-conferencing will be commonplace. Cost effective and power efficient support for these applications will be required. Looking forward to the next generation of mobile computing, computation requirements will increase by one to three orders of magnitude due to higher data rates, increased complexity algorithms, and greater computation diversity but the power requirements will be just as stringent to ensure reasonable battery lifetimes. The design of the next generation of mobile platforms must address three critical challenges: efficiency, programmability, and adaptivity. The computational efficiency of existing solutions is inadequate and straightforward scaling by increasing the number of cores or the amount of data-level parallelism will not suffice. Programmability provides the opportunity for a single platform to support multiple applications and even multiple standards within each application domain. Programmability also provides: faster time to market as hardware and software development can proceed in parallel; the ability to fix bugs and add features after manufacturing; and, higher chip volumes as a single platform can support a family of mobile devices. Lastly, hardware adaptivity is necessary to maintain efficiency as the computational characteristics of the applications change. Current solutions are tailored specifically for wireless signal processing algorithms, but lose their efficiency when other application domains like high definition video are processed. This thesis addresses these challenges by presenting analysis of next generation mobile signal processing applications and proposing an advanced signal processing architecture to deal with the stringent requirements. An application-centric design approach is taken to design our architecture. First, a next generation wireless protocol and high definition video is analyzed and algorithmic characterizations discussed. From these characterizations, key architectural implications are presented, which form the basis for the advanced signal processor architecture, AnySP.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86344/1/mwoh_1.pd

Architecture Independent Timing Speculation Techniques in VLSI Circuits.

Conventional digital circuits must ensure correct operation throughout a wide range of operating conditions including process, voltage, and temperature variation. These conditions have an effect on circuit delays, and safety margins must be put in place which come at a power and performance cost. The Razor system proposed eliminating these timing margins by running a circuit with occasional timing errors and correcting the errors when they occur. Several existing Razor style designs have been proposed, however prior to this work, Razor could not be applied blindly or automatically to designs, as the various error correction schemes modified the architecture of the target design. Because of the architectural invasiveness and design complexities of these techniques, no published Razor style system had been applied to a complete existing commercial processor. Additionally, in all prior Razor-style systems, there is a fundamental tradeoff between speculation window and short path, or minimum delay, constraints, limiting the technique’s effectiveness. This thesis introduces the concept of Razor using two-phase latch based timing. By identifying and utilizing time borrowing as an error correction mechanism, it allows for Razor to be applied without the need to reload data or replay instructions. This allows for Razor to be blindly and automatically applied to existing designs without detailed knowledge of internal architecture. Additionally, latch based Razor allows for large speculation windows, up to 100% of nominal circuit delay, because it breaks the connection between minimum delay constraints and speculation window. By demonstrating how to transform conventional flip-flop based designs, including those which make use of clock gating, to two-phase latch based timing, Razor can be automatically added to a large set of existing digital designs. Two forms of latch based Razor are proposed. First, Bubble Razor involves rippling stall cycles throughout a circuit in response to timing errors and is applied to the ARM Cortex-M3 processor, the first ever application of a Razor technique to a complete, existing processor design. Additional work applies Bubble Razor to the ARM Cortex-R4 processor. The second latch based Razor technique, Voltage Razor, uses voltage boosting to correct for timing errors.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102461/1/mfojtik_1.pd

Energy efficient hardware acceleration of multimedia processing tools

The world of mobile devices is experiencing an ongoing trend of feature enhancement and generalpurpose multimedia platform convergence. This trend poses many grand challenges, the most pressing being their limited battery life as a consequence of delivering computationally demanding features. The envisaged mobile application features can be considered to be accelerated by a set of underpinning hardware blocks Based on the survey that this thesis presents on modem video compression standards and their associated enabling technologies, it is concluded that tight energy and throughput constraints can still be effectively tackled at algorithmic level in order to design re-usable optimised hardware acceleration cores. To prove these conclusions, the work m this thesis is focused on two of the basic enabling technologies that support mobile video applications, namely the Shape Adaptive Discrete Cosine Transform (SA-DCT) and its inverse, the SA-IDCT. The hardware architectures presented in this work have been designed with energy efficiency in mind. This goal is achieved by employing high level techniques such as redundant computation elimination, parallelism and low switching computation structures. Both architectures compare favourably against the relevant pnor art in the literature. The SA-DCT/IDCT technologies are instances of a more general computation - namely, both are Constant Matrix Multiplication (CMM) operations. Thus, this thesis also proposes an algorithm for the efficient hardware design of any general CMM-based enabling technology. The proposed algorithm leverages the effective solution search capability of genetic programming. A bonus feature of the proposed modelling approach is that it is further amenable to hardware acceleration. Another bonus feature is an early exit mechanism that achieves large search space reductions .Results show an improvement on state of the art algorithms with future potential for even greater savings

Embedding Logic and Non-volatile Devices in CMOS Digital Circuits for Improving Energy Efficiency

abstract: Static CMOS logic has remained the dominant design style of digital systems for more than four decades due to its robustness and near zero standby current. Static CMOS logic circuits consist of a network of combinational logic cells and clocked sequential elements, such as latches and flip-flops that are used for sequencing computations over time. The majority of the digital design techniques to reduce power, area, and leakage over the past four decades have focused almost entirely on optimizing the combinational logic. This work explores alternate architectures for the flip-flops for improving the overall circuit performance, power and area. It consists of three main sections. First, is the design of a multi-input configurable flip-flop structure with embedded logic. A conventional D-type flip-flop may be viewed as realizing an identity function, in which the output is simply the value of the input sampled at the clock edge. In contrast, the proposed multi-input flip-flop, named PNAND, can be configured to realize one of a family of Boolean functions called threshold functions. In essence, the PNAND is a circuit implementation of the well-known binary perceptron. Unlike other reconfigurable circuits, a PNAND can be configured by simply changing the assignment of signals to its inputs. Using a standard cell library of such gates, a technology mapping algorithm can be applied to transform a given netlist into one with an optimal mixture of conventional logic gates and threshold gates. This approach was used to fabricate a 32-bit Wallace Tree multiplier and a 32-bit booth multiplier in 65nm LP technology. Simulation and chip measurements show more than 30% improvement in dynamic power and more than 20% reduction in core area. The functional yield of the PNAND reduces with geometry and voltage scaling. The second part of this research investigates the use of two mechanisms to improve the robustness of the PNAND circuit architecture. One is the use of forward and reverse body biases to change the device threshold and the other is the use of RRAM devices for low voltage operation. The third part of this research focused on the design of flip-flops with non-volatile storage. Spin-transfer torque magnetic tunnel junctions (STT-MTJ) are integrated with both conventional D-flipflop and the PNAND circuits to implement non-volatile logic (NVL). These non-volatile storage enhanced flip-flops are able to save the state of system locally when a power interruption occurs. However, manufacturing variations in the STT-MTJs and in the CMOS transistors significantly reduce the yield, leading to an overly pessimistic design and consequently, higher energy consumption. A detailed analysis of the design trade-offs in the driver circuitry for performing backup and restore, and a novel method to design the energy optimal driver for a given yield is presented. Efficient designs of two nonvolatile flip-flop (NVFF) circuits are presented, in which the backup time is determined on a per-chip basis, resulting in minimizing the energy wastage and satisfying the yield constraint. To achieve a yield of 98%, the conventional approach would have to expend nearly 5X more energy than the minimum required, whereas the proposed tunable approach expends only 26% more energy than the minimum. A non-volatile threshold gate architecture NV-TLFF are designed with the same backup and restore circuitry in 65nm technology. The embedded logic in NV-TLFF compensates performance overhead of NVL. This leads to the possibility of zero-overhead non-volatile datapath circuits. An 8-bit multiply-and- accumulate (MAC) unit is designed to demonstrate the performance benefits of the proposed architecture. Based on the results of HSPICE simulations, the MAC circuit with the proposed NV-TLFF cells is shown to consume at least 20% less power and area as compared to the circuit designed with conventional DFFs, without sacrificing any performance.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201