Semi-Supervised Deep Learning for Microcontroller Performance Screening

In safety-critical applications, microcontrollers must satisfy strict quality constraints and performances in terms of F_max (the maximum operating frequency). Data extracted from on-chip ring oscillators (ROs) can model the F_max of integrated circuits using machine learning models. Those models are suitable for the performance screening process. Acquiring data from the ROs is a fast process that leads to many unlabeled data. Contrarily, the labeling phase (i.e., acquiring F_max) is a time-consuming and costly task, that leads to a small set of labeled data. This paper presents deep-learning-based methodologies to cope with the low number of labeled data in microcontroller performance screening. We propose a method that takes advantage of the high number of unlabeled samples in a semi-supervised learning fashion. We derive deep feature extractor models that project data into higher dimensional spaces and use the data feature embedding to face the performance prediction problem with simple linear regression. Experiments showed that the proposed models outperformed state-of-the-art methodologies in terms of prediction error and permitted us to use a significantly smaller number of devices to be characterized, thus reducing the time needed to build ML models by a factor of six with respect to baseline approaches

A Multi-Label Active Learning Framework for Microcontroller Performance Screening

In safety-critical applications, microcontrollers have to be tested to satisfy strict quality and performances constraints. It has been demonstrated that on-chip ring oscillators can be be used as speed monitors to reliably predict the performances. However, any machine-learning model is likely to be inaccurate if trained on an inadequate dataset, and labeling data for training is quite a costly process. In this paper, we present a methodology based on active learning to select the best samples to be included in the training set, significantly reducing the time and cost required. Moreover, since different speed measurements are available, we designed a multi-label technique to take advantage of their correlations. Experimental results demonstrate that the approach halves the training-set size, with respect to a random labelling, while it increases the predictive accuracy, with respect to standard single-label machine-learning models

로직 및 피지컬 합성에서의 타이밍 분석과 최적화

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 김태환.Timing analysis is one of the necessary steps in the development of a semiconductor circuit. In addition, it is increasingly important in the advanced process technologies due to various factors, including the increase of process–voltage–temperature variation. This dissertation addresses three problems related to timing analysis and optimization in logic and physical synthesis. Firstly, most static timing analysis today are based on conventional fixed flip-flop timing models, in which every flip-flop is assumed to have a fixed clock-to-Q delay. However, setup and hold skews affect the clock-to-Q delay in reality. In this dissertation, I propose a mathematical formulation to solve the problem and apply it to the clock skew scheduling problems as well as to the analysis of a given circuit, with a scalable speedup technique. Secondly, near-threshold computing is one of the promising concepts for energy-efficient operation of VLSI systems, but wide performance variation and nonlinearity to process variations block the proliferation. To cope with this, I propose a holistic hardware performance monitoring methodology for accurate timing prediction in a near-threshold voltage regime and advanced process technology. Lastly, an asynchronous circuit is one of the alternatives to the conventional synchronous style, and asynchronous pipeline circuit especially attractive because of its small design effort. This dissertation addresses the synthesis problem of lightening two-phase bundled-data asynchronous pipeline controllers, in which delay buffers are essential for guaranteeing the correct handshaking operation but incurs considerable area increase.타이밍 분석은 반도체 회로 개발 필수 과정 중 하나로, 최신 공정일수록 공정-전압-온도 변이 증가를 포함한 다양한 요인으로 하여금 그 중요성이 커지고 있다. 본 논문에서는 로직 및 피지컬 합성과 관련하여 세 가지 타이밍 분석 및 최적화 문제에 대해 다룬다. 첫째로, 오늘날 대부분의 정적 타이밍 분석은 모든 플립-플롭의 클럭-출력 딜레이가 고정된 값이라는 가정을 바탕으로 이루어졌다. 하지만 실제 클럭-출력 딜레이는 해당 플립-플롭의 셋업 및 홀드 스큐에 영향을 받는다. 본 논문에서는 이러한 특성을 수학적으로 정리하였으며, 이를 확장 가능한 속도 향상 기법과 더불어 주어진 회로의 타이밍 분석 및 클럭 스큐 스케쥴링 문제에 적용하였다. 둘째로, 유사 문턱 연산은 초고집적 회로 동작의 에너지 효율을 끌어 올릴 수 있다는 점에서 각광받지만, 큰 폭의 성능 변이 및 비선형성 때문에 널리 활용되고 있지 않다. 이를 해결하기 위해 유사 문턱 전압 영역 및 최신 공정 노드에서 보다 정확한 타이밍 예측을 위한 하드웨어 성능 모니터링 방법론 전반을 제안하였다. 마지막으로, 비동기 회로는 기존 동기 회로의 대안 중 하나로, 그 중에서도 비동기 파이프라인 회로는 비교적 적은 설계 노력만으로도 구현 가능하다는 장점이 있다. 본 논문에서는 2위상 묶음 데이터 프로토콜 기반 비동기 파이프라인 컨트롤러 상에서, 정확한 핸드셰이킹 통신을 위해 삽입된 딜레이 버퍼에 의한 면적 증가를 완화할 수 있는 합성 기법을 제시하였다.1 INTRODUCTION 1 1.1 Flexible Flip-Flop Timing Model 1 1.2 Hardware Performance Monitoring Methodology 4 1.3 Asynchronous Pipeline Controller 10 1.4 Contributions of this Dissertation 15 2 ANALYSIS AND OPTIMIZATION CONSIDERING FLEXIBLE FLIP-FLOP TIMING MODEL 17 2.1 Preliminaries 17 2.1.1 Terminologies 17 2.1.2 Timing Analysis 20 2.1.3 Clock-to-Q Delay Surface Modeling 21 2.2 Clock-to-Q Delay Interval Analysis 22 2.2.1 Derivation 23 2.2.2 Additional Constraints 26 2.2.3 Analysis: Finding Minimum Clock Period 28 2.2.4 Optimization: Clock Skew Scheduling 30 2.2.5 Scalable Speedup Technique 33 2.3 Experimental Results 37 2.3.1 Application to Minimum Clock Period Finding 37 2.3.2 Application to Clock Skew Scheduling 39 2.3.3 Efficacy of Scalable Speedup Technique 43 2.4 Summary 44 3 HARDWARE PERFORMANCE MONITORING METHODOLOGY AT NTC AND ADVANCED TECHNOLOGY NODE 45 3.1 Overall Flow of Proposed HPM Methodology 45 3.2 Prerequisites to HPM Methodology 47 3.2.1 BEOL Process Variation Modeling 47 3.2.2 Surrogate Model Preparation 49 3.3 HPM Methodology: Design Phase 52 3.3.1 HPM2PV Model Construction 52 3.3.2 Optimization of Monitoring Circuits Configuration 54 3.3.3 PV2CPT Model Construction 58 3.4 HPM Methodology: Post-Silicon Phase 60 3.4.1 Transfer Learning in Silicon Characterization Step 60 3.4.2 Procedures in Volume Production Phase 61 3.5 Experimental Results 62 3.5.1 Experimental Setup 62 3.5.2 Exploration of Monitoring Circuits Configuration 64 3.5.3 Effectiveness of Monitoring Circuits Optimization 66 3.5.4 Considering BEOL PVs and Uncertainty Learning 68 3.5.5 Comparison among Different Prediction Flows 69 3.5.6 Effectiveness of Prediction Model Calibration 71 3.6 Summary 73 4 LIGHTENING ASYNCHRONOUS PIPELINE CONTROLLER 75 4.1 Preliminaries and State-of-the-Art Work 75 4.1.1 Bundled-data vs. Dual-rail Asynchronous Circuits 75 4.1.2 Two-phase vs. Four-phase Bundled-data Protocol 76 4.1.3 Conventional State-of-the-Art Pipeline Controller Template 77 4.2 Delay Path Sharing for Lightening Pipeline Controller Template 78 4.2.1 Synthesizing Sharable Delay Paths 78 4.2.2 Validating Logical Correctness for Sharable Delay Paths 80 4.2.3 Reformulating Timing Constraints of Controller Template 81 4.2.4 Minimally Allocating Delay Buffers 87 4.3 In-depth Pipeline Controller Template Synthesis with Delay Path Reusing 88 4.3.1 Synthesizing Delay Path Units 88 4.3.2 Validating Logical Correctness of Delay Path Units 89 4.3.3 Updating Timing Constraints for Delay Path Units 91 4.3.4 In-depth Synthesis Flow Utilizing Delay Path Units 95 4.4 Experimental Results 99 4.4.1 Environment Setup 99 4.4.2 Piecewise Linear Modeling of Delay Path Unit Area 99 4.4.3 Comparison of Power, Performance, and Area 102 4.5 Summary 107 5 CONCLUSION 109 5.1 Chapter 2 109 5.2 Chapter 3 110 5.3 Chapter 4 110 Abstract (In Korean) 127Docto

Circuits and Systems Advances in Near Threshold Computing

Modern society is witnessing a sea change in ubiquitous computing, in which people have embraced computing systems as an indispensable part of day-to-day existence. Computation, storage, and communication abilities of smartphones, for example, have undergone monumental changes over the past decade. However, global emphasis on creating and sustaining green environments is leading to a rapid and ongoing proliferation of edge computing systems and applications. As a broad spectrum of healthcare, home, and transport applications shift to the edge of the network, near-threshold computing (NTC) is emerging as one of the promising low-power computing platforms. An NTC device sets its supply voltage close to its threshold voltage, dramatically reducing the energy consumption. Despite showing substantial promise in terms of energy efficiency, NTC is yet to see widescale commercial adoption. This is because circuits and systems operating with NTC suffer from several problems, including increased sensitivity to process variation, reliability problems, performance degradation, and security vulnerabilities, to name a few. To realize its potential, we need designs, techniques, and solutions to overcome these challenges associated with NTC circuits and systems. The readers of this book will be able to familiarize themselves with recent advances in electronics systems, focusing on near-threshold computing

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

Active timing margin management to improve microprocessor power efficiency

Improving power/performance efficiency is critical for today’s micro- processors. From edge devices to datacenters, lower power or higher performance always produces better systems, measured by lower cost of ownership or longer battery time. This thesis studies improving microprocessor power/performance efficiency by optimizing the pipeline timing margin. In particular, this thesis focuses on improving the efficacy of Active Timing Margin, a young technology that dynamically adjusts the margin. Active timing margin trims down the pipeline timing margin with a control loop that adjusts voltage and frequency based on real-time chip environment monitoring. The key insight of this thesis is that in order to maximize active timing margin’s efficiency enhancement benefits, synergistic management from processor architecture design and system software scheduling are needed. To that end, this thesis covers the major consumers of pipeline timing margin, including temperature, voltage, and process variation. For temperature variation, the thesis proposes a table-lookup based active timing margin mechanism, and an associated temperature management scheme to minimize power consumption. For voltage variation, the thesis characterizes the limiting factors of adaptive clocking’s power saving and proposes application scheduling to maximize total system power reduction. For process variation, the thesis proposes core-level adaptive clocking reconfiguration to automatically expose inter-core variation and discusses workload scheduling and throttling management to control critical application performance. The author believes the optimization presented in this thesis can potentially benefit a variety of processor architectures as the conclusions are based on the solid measurement on state-of-the-art processors, and the research objective, active timing margin, already has wide applicability in the latest microprocessors by the time this thesis is written.Electrical and Computer Engineerin