Instruction Fetching: Coping with Code Bloat
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Efficient Processing of Deep Neural Networks
Efficient Processing of Deep Neural Networks Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, Joel Emer Massachusetts Institute of Technology Reference: V. Sze, Y.-H.Chen, T.-J. Yang, J. S. Emer, ”Efficient Processing of Deep Neural Networks,” Synthesis Lectures on Computer Architecture, Morgan & Claypool Publishers, 2020 For book updates, sign up for mailing list at http://mailman.mit.edu/mailman/listinfo/eems-news June 15, 2020 Abstract This book provides a structured treatment of the key principles and techniques for enabling efficient process- ing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the- art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas. 1 Contents Preface 9 I Understanding Deep Neural Networks 13 1 Introduction 14 1.1 Background on Deep Neural Networks . -
UNIVERSITY of CALIFORNIA, SAN DIEGO Holistic Design for Multi-Core Architectures a Dissertation Submitted in Partial Satisfactio
UNIVERSITY OF CALIFORNIA, SAN DIEGO Holistic Design for Multi-core Architectures A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Computer Science (Computer Engineering) by Rakesh Kumar Committee in charge: Professor Dean Tullsen, Chair Professor Brad Calder Professor Fred Chong Professor Rajesh Gupta Dr. Norman P. Jouppi Professor Andrew Kahng 2006 Copyright Rakesh Kumar, 2006 All rights reserved. The dissertation of Rakesh Kumar is approved, and it is acceptable in quality and form for publication on micro- film: Chair University of California, San Diego 2006 iii DEDICATIONS This dissertation is dedicated to friends, family, labmates, and mentors { the ones who taught me, indulged me, loved me, challenged me, and laughed with me, while I was also busy working on my thesis. To Professor Dean Tullsen for teaching me the values of humility, kind- ness,and caring while trying to teach me football and computer architecture. For always encouraging me to do the right thing. For always letting me be myself. For always believing in me. For always challenging me to dream big. For all his wisdom. And for being an adviser in the truest sense, and more. To Professor Brad Calder. For always caring about me. For being an inspiration. For his trust. For making me believe in myself. For his lies about me getting better at system administration and foosball even though I never did. To Dr Partha Ranganathan. For always being there for me when I would get down on myself. And that happened often. For the long discussions on life, work, and happiness. -
Appendix M Historical Perspectives and References
M.1 Introduction M-2 M.2 The Early Development of Computers (Chapter 1) M-2 M.3 The Development of Memory Hierarchy and Protection (Chapter 2 and Appendix B) M-9 M.4 The Evolution of Instruction Sets (Appendices A, J, and K) M-17 M.5 The Development of Pipelining and Instruction-Level Parallelism (Chapter 3 and Appendices C and H) M-27 M.6 The Development of SIMD Supercomputers, Vector Computers, Multimedia SIMD Instruction Extensions, and Graphical Processor Units (Chapter 4) M-45 M.7 The History of Multiprocessors and Parallel Processing (Chapter 5 and Appendices F, G, and I) M-55 M.8 The Development of Clusters (Chapter 6) M-74 M.9 Historical Perspectives and References M-79 M.10 The History of Magnetic Storage, RAID, and I/O Buses (Appendix D) M-84 M Historical Perspectives and References If … history … teaches us anything, it is that man in his quest for knowledge and progress is determined and cannot be deterred. John F. Kennedy Address at Rice University (1962) Those who cannot remember the past are condemned to repeat it. George Santayana The Life of Reason (1905), Vol. 2, Chapter 3 M-2 ■ Appendix M Historical Perspectives and References M.1 Introduction This appendix provides historical background on some of the key ideas presented in the chapters. We may trace the development of an idea through a series of machines or describe significant projects. If you are interested in examining the initial development of an idea or machine or are interested in further reading, references are provided at the end of each section. -
Instruction Fetching: Coping with Code Bloat
To appear in the 22nd International Symposium on Computer Architecture, Santa Magherita Ligure, Italy, June, 1995. Instruction Fetching: Coping with Code Bloat Richard Uhlig David Nagle German National Research Center for Computer Science ECE Department, Carnegie Mellon University Schloβ Birlinghoven, 53757 Sankt Augustin, Germany 4902 Forbes Ave, Pittsburgh, PA 15213 [email protected] [email protected] Trevor Mudge and Stuart Sechrest Joel Emer EECS Department, University of Michigan Digital Equipment Corporation 1301 Beal Ave., Ann Arbor, Michigan 48109-2122 77 Reed Road HLO2-3/J3, Hudson, MA 01749 {tnm,sechrest}@eecs.umich.edu [email protected] Abstract trends on one important aspect of memory-system design: the fetching of instructions. Previous research has shown that the SPEC benchmarks As application and operating system software evolves to achieve low miss ratios in relatively small instruction caches. This include more features and to become more portable, maintainable paper presents evidence that current software-development prac- and reliable, it also tends to consume more memory resources. tices produce applications that exhibit substantially higher The “bloating” of code affects a memory-system hierarchy at all instruction-cache miss ratios than do the SPEC benchmarks. To levels and in a variety of ways: the larger static sizes of program represent these trends, we have assembled a collection of applica- executables occupy more disk space; the larger working sets of tions, called the Instruction Benchmark Suite (IBS), that provides bloated programs require more physical main memory; bloated a better test of instruction-cache performance. We discuss the programs use virtual memory in a more sparse and fragmented rationale behind the design of IBS and characterize its behavior manner, making their page-table entries less likely to fit in TLBs; relative to the SPEC benchmark suite. -
Loose Loops Sink Chips
Loose Loops Sink Chips Eric Borch Eric Tune Intel Corporation, VSSAD University of California, San Diego [email protected] Department of Computer Science [email protected] Srilatha Manne Joel Emer Intel Corporation, VSSAD ] [srilatha.manne, joel.emer @intel.com Abstract Stage 11 Stage 21 Stage N Loose Loop This paper explores the concept of micro-architectural Tight Loop loops and discusses their impact on processor pipelines. In particular, we establish the relationship between loose loops and pipeline length and configuration, and show their 2 impact on performance. We then evaluate the load reso- Clock lution loop in detail and propose the distributed register Initiation Resolution algorithm (DRA) as a way of reducing this loop. It de- Stage Stage creases the performance loss due to load mis-speculations Loop Length by reducing the issue-to-execute latency in the pipeline. A new loose loop is introduced into the pipeline by the DRA, Figure 1. Micro-architectural loops but the frequency of mis-speculations is very low. The re- duction in latency from issue to execute, along with a low mis-speculation rate in the DRAresult in up to a 4% to 15% loop resolution stage is the execute stage. Loop length is de- improvement in performance using a detailed architectural fined to be the number of stages traversed by a loop, and the simulator. feedback delay is the time required to communicate from the resolution stage to the initiation stage. Loop delay is the sum of the loop length and feedback delay. Loops with 1 Introduction a loop delay of one are referred to as tight loops; all other loops are referred to as loose loops.