Machine Learning Inspired High Performance And Energy Efficient Heterogeneous Manycore Chip Design
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| Author | : Sudeep Pasricha |
| Publisher | : Springer Nature |
| Total Pages | : 481 |
| Release | : 2023-10-09 |
| Genre | : Technology & Engineering |
| ISBN | : 3031399323 |
This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.
| Author | : Leslie Pack Kaelbling |
| Publisher | : MIT Press |
| Total Pages | : 206 |
| Release | : 1993 |
| Genre | : Computers |
| ISBN | : 9780262111744 |
Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics and machine learning. Presenting interesting, new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial and error experience with an external world. The text is a detailed exploration of the problem of learning action strategies in the context of designing embedded systems that adapt their behaviour to a complex, changing environment. Such systems include mobile robots, factory process controllers and long-term software databases.
| Author | : Milin Zhang |
| Publisher | : CRC Press |
| Total Pages | : 776 |
| Release | : 2022-09-01 |
| Genre | : Science |
| ISBN | : 1000791920 |
Low Power Circuit Design Using Advanced CMOS Technology is a summary of lectures from the first Advanced CMOS Technology Summer School (ACTS) 2017. The slides are selected from the handouts, while the text was edited according to the lecturers talk.ACTS is a joint activity supported by the IEEE Circuit and System Society (CASS) and the IEEE Solid-State Circuits Society (SSCS). The goal of the school is to provide society members as well researchers and engineers from industry the opportunity to learn about new emerging areas from leading experts in the field. ACTS is an example of high-level continuous education for junior engineers, teachers in academe, and students. ACTS was the results of a successful collaboration between societies, the local chapter leaders, and industry leaders. This summer school was the brainchild of Dr. Zhihua Wang, with strong support from volunteers from both the IEEE SSCS and CASS. In addition, the local companies, Synopsys China and Beijing IC Park, provided support.This first ACTS was held in the summer 2017 in Beijing. The lectures were given by academic researchers and industry experts, who presented each 6-hour long lectures on topics covering process technology, EDA skill, and circuit and layout design skills. The school was hosted and organized by the CASS Beijing Chapter, SSCS Beijing Chapter, and SSCS Tsinghua Student Chapter. The co-chairs of the first ACTS were Dr. Milin Zhang, Dr. Hanjun Jiang and Dr. Liyuan Liu. The first ACTS was a great success as illustrated by the many participants from all over China as well as by the publicity it has been received in various media outlets, including Xinhua News, one of the most popular news channels in China.
| Author | : Anup Das |
| Publisher | : Frontiers Media SA |
| Total Pages | : 235 |
| Release | : 2024-08-22 |
| Genre | : Science |
| ISBN | : 2832553184 |
Spiking Neural Networks (SNN) closely imitate biological networks. Information processing occurs in both spatial and temporal manner, making SNN extremely interesting for the pertinent mimicking of the biological brain. Biological brains code and transmit the sensory information in the form of spikes that capture the spatial and temporal information of the environment with amazing precision. This information is processed in an asynchronous way by the neural layer performing recognition of complex spatio-temporal patterns with sub-milliseconds delay and at with a power budget in the order of 20W. The efficient spike coding mechanism and the asynchronous and sparse processing and communication of spikes seems to be key in the energy efficiency and high-speed computation capabilities of biological brains. SNN low-power and event-based computation make them more attractive when compared to other artificial neural networks (ANN).
| Author | : Ibrahim (Abe) M. Elfadel |
| Publisher | : Springer |
| Total Pages | : 697 |
| Release | : 2019-03-15 |
| Genre | : Technology & Engineering |
| ISBN | : 3030046664 |
This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other....As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center
| Author | : Vivienne Sze |
| Publisher | : Springer Nature |
| Total Pages | : 254 |
| Release | : 2022-05-31 |
| Genre | : Technology & Engineering |
| ISBN | : 3031017668 |
This book provides a structured treatment of the key principles and techniques for enabling efficient processing 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.
| Author | : Shaojun Wei |
| Publisher | : Springer Nature |
| Total Pages | : 316 |
| Release | : 2022-10-20 |
| Genre | : Technology & Engineering |
| ISBN | : 9811969949 |
This is the first book of a two-volume book set which introduces software defined chips. In this book, it introduces the conceptual evolution of software defined chips from the development of integrated circuits and computing architectures. Technical principles, characteristics and key issues of software defined chips are systematically analyzed. The hardware architecture design methods are described involving architecture design primitives, hardware design spaces and agile design methods. From the perspective of the compilation system, the complete process from high-level language to configuration contexts is introduced in detail. This book is suitable for scientists and researchers in the areas of electrical and electronic engineering and computer science. Postgraduate students, practitioners and professionals in related areas are also potentially interested in the topic of this book.
| Author | : Katrin Amunts |
| Publisher | : Springer Nature |
| Total Pages | : 159 |
| Release | : 2021-07-20 |
| Genre | : Computers |
| ISBN | : 3030824276 |
This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures.
| Author | : Stefanos Kaxiras |
| Publisher | : Morgan & Claypool Publishers |
| Total Pages | : 220 |
| Release | : 2008 |
| Genre | : Computers |
| ISBN | : 1598292080 |
In the last few years, power dissipation has become an important design constraint, on par with performance, in the design of new computer systems. Whereas in the past, the primary job of the computer architect was to translate improvements in operating frequency and transistor count into performance, now power efficiency must be taken into account at every step of the design process. While for some time, architects have been successful in delivering 40% to 50% annual improvement in processor performance, costs that were previously brushed aside eventually caught up. The most critical of these costs is the inexorable increase in power dissipation and power density in processors. Power dissipation issues have catalyzed new topic areas in computer architecture, resulting in a substantial body of work on more power-efficient architectures. Power dissipation coupled with diminishing performance gains, was also the main cause for the switch from single-core to multi-core architectures and a slowdown in frequency increase. This book aims to document some of the most important architectural techniques that were invented, proposed, and applied to reduce both dynamic power and static power dissipation in processors and memory hierarchies. A significant number of techniques have been proposed for a wide range of situations and this book synthesizes those techniques by focusing on their common characteristics.
| Author | : Robert Robey |
| Publisher | : Simon and Schuster |
| Total Pages | : 702 |
| Release | : 2021-08-24 |
| Genre | : Computers |
| ISBN | : 1638350388 |
Parallel and High Performance Computing offers techniques guaranteed to boost your code’s effectiveness. Summary Complex calculations, like training deep learning models or running large-scale simulations, can take an extremely long time. Efficient parallel programming can save hours—or even days—of computing time. Parallel and High Performance Computing shows you how to deliver faster run-times, greater scalability, and increased energy efficiency to your programs by mastering parallel techniques for multicore processor and GPU hardware. About the technology Write fast, powerful, energy efficient programs that scale to tackle huge volumes of data. Using parallel programming, your code spreads data processing tasks across multiple CPUs for radically better performance. With a little help, you can create software that maximizes both speed and efficiency. About the book Parallel and High Performance Computing offers techniques guaranteed to boost your code’s effectiveness. You’ll learn to evaluate hardware architectures and work with industry standard tools such as OpenMP and MPI. You’ll master the data structures and algorithms best suited for high performance computing and learn techniques that save energy on handheld devices. You’ll even run a massive tsunami simulation across a bank of GPUs. What's inside Planning a new parallel project Understanding differences in CPU and GPU architecture Addressing underperforming kernels and loops Managing applications with batch scheduling About the reader For experienced programmers proficient with a high-performance computing language like C, C++, or Fortran. About the author Robert Robey works at Los Alamos National Laboratory and has been active in the field of parallel computing for over 30 years. Yuliana Zamora is currently a PhD student and Siebel Scholar at the University of Chicago, and has lectured on programming modern hardware at numerous national conferences. Table of Contents PART 1 INTRODUCTION TO PARALLEL COMPUTING 1 Why parallel computing? 2 Planning for parallelization 3 Performance limits and profiling 4 Data design and performance models 5 Parallel algorithms and patterns PART 2 CPU: THE PARALLEL WORKHORSE 6 Vectorization: FLOPs for free 7 OpenMP that performs 8 MPI: The parallel backbone PART 3 GPUS: BUILT TO ACCELERATE 9 GPU architectures and concepts 10 GPU programming model 11 Directive-based GPU programming 12 GPU languages: Getting down to basics 13 GPU profiling and tools PART 4 HIGH PERFORMANCE COMPUTING ECOSYSTEMS 14 Affinity: Truce with the kernel 15 Batch schedulers: Bringing order to chaos 16 File operations for a parallel world 17 Tools and resources for better code
