Learning In Energy Efficient Neuromorphic Computing Algorithm And Architecture Co Design

Author : Nan Zheng,Pinaki Mazumder
Publisher : John Wiley & Sons
Page : 389 pages
File Size : 55,9 Mb
Release : 2019-10-18
Category : Computers
ISBN : 9781119507406

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Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design by Nan Zheng,Pinaki Mazumder Pdf

Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning applications This book focuses on how to build energy-efficient hardware for neural networks with learning capabilities—and provides co-design and co-optimization methodologies for building hardware neural networks that can learn. Presenting a complete picture from high-level algorithm to low-level implementation details, Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design also covers many fundamentals and essentials in neural networks (e.g., deep learning), as well as hardware implementation of neural networks. The book begins with an overview of neural networks. It then discusses algorithms for utilizing and training rate-based artificial neural networks. Next comes an introduction to various options for executing neural networks, ranging from general-purpose processors to specialized hardware, from digital accelerator to analog accelerator. A design example on building energy-efficient accelerator for adaptive dynamic programming with neural networks is also presented. An examination of fundamental concepts and popular learning algorithms for spiking neural networks follows that, along with a look at the hardware for spiking neural networks. Then comes a chapter offering readers three design examples (two of which are based on conventional CMOS, and one on emerging nanotechnology) to implement the learning algorithm found in the previous chapter. The book concludes with an outlook on the future of neural network hardware. Includes cross-layer survey of hardware accelerators for neuromorphic algorithms Covers the co-design of architecture and algorithms with emerging devices for much-improved computing efficiency Focuses on the co-design of algorithms and hardware, which is especially critical for using emerging devices, such as traditional memristors or diffusive memristors, for neuromorphic computing Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design is an ideal resource for researchers, scientists, software engineers, and hardware engineers dealing with the ever-increasing requirement on power consumption and response time. It is also excellent for teaching and training undergraduate and graduate students about the latest generation neural networks with powerful learning capabilities.

Author : Nan Zheng,Pinaki Mazumder
Publisher : John Wiley & Sons
Page : 296 pages
File Size : 51,5 Mb
Release : 2019-12-31
Category : Computers
ISBN : 9781119507383

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Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design by Nan Zheng,Pinaki Mazumder Pdf

Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning applications This book focuses on how to build energy-efficient hardware for neural networks with learning capabilities—and provides co-design and co-optimization methodologies for building hardware neural networks that can learn. Presenting a complete picture from high-level algorithm to low-level implementation details, Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design also covers many fundamentals and essentials in neural networks (e.g., deep learning), as well as hardware implementation of neural networks. The book begins with an overview of neural networks. It then discusses algorithms for utilizing and training rate-based artificial neural networks. Next comes an introduction to various options for executing neural networks, ranging from general-purpose processors to specialized hardware, from digital accelerator to analog accelerator. A design example on building energy-efficient accelerator for adaptive dynamic programming with neural networks is also presented. An examination of fundamental concepts and popular learning algorithms for spiking neural networks follows that, along with a look at the hardware for spiking neural networks. Then comes a chapter offering readers three design examples (two of which are based on conventional CMOS, and one on emerging nanotechnology) to implement the learning algorithm found in the previous chapter. The book concludes with an outlook on the future of neural network hardware. Includes cross-layer survey of hardware accelerators for neuromorphic algorithms Covers the co-design of architecture and algorithms with emerging devices for much-improved computing efficiency Focuses on the co-design of algorithms and hardware, which is especially critical for using emerging devices, such as traditional memristors or diffusive memristors, for neuromorphic computing Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design is an ideal resource for researchers, scientists, software engineers, and hardware engineers dealing with the ever-increasing requirement on power consumption and response time. It is also excellent for teaching and training undergraduate and graduate students about the latest generation neural networks with powerful learning capabilities.

Author : Shuangming Yang
Publisher : Springer Nature
Page : 256 pages
File Size : 47,7 Mb
Release : 2024-06-30
Category : Electronic
ISBN : 9783031578731

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Neuromorphic Intelligence by Shuangming Yang Pdf

Author : Vivienne Sze,Yu-Hsin Chen,Tien-Ju Yang,Joel S. Emer
Publisher : Springer Nature
Page : 254 pages
File Size : 47,5 Mb
Release : 2022-05-31
Category : Technology & Engineering
ISBN : 9783031017667

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Efficient Processing of Deep Neural Networks by Vivienne Sze,Yu-Hsin Chen,Tien-Ju Yang,Joel S. Emer Pdf

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 : Sudeep Pasricha,Muhammad Shafique
Publisher : Springer Nature
Page : 418 pages
File Size : 45,9 Mb
Release : 2023-11-01
Category : Technology & Engineering
ISBN : 9783031195686

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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing by Sudeep Pasricha,Muhammad Shafique Pdf

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.

Author : Jawad Haj-Yahya,Avi Mendelson,Yosi Ben Asher,Anupam Chattopadhyay
Publisher : Springer
Page : 165 pages
File Size : 48,5 Mb
Release : 2018-03-22
Category : Technology & Engineering
ISBN : 9789811085543

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Energy Efficient High Performance Processors by Jawad Haj-Yahya,Avi Mendelson,Yosi Ben Asher,Anupam Chattopadhyay Pdf

This book explores energy efficiency techniques for high-performance computing (HPC) systems using power-management methods. Adopting a step-by-step approach, it describes power-management flows, algorithms and mechanism that are employed in modern processors such as Intel Sandy Bridge, Haswell, Skylake and other architectures (e.g. ARM). Further, it includes practical examples and recent studies demonstrating how modem processors dynamically manage wide power ranges, from a few milliwatts in the lowest idle power state, to tens of watts in turbo state. Moreover, the book explains how thermal and power deliveries are managed in the context this huge power range. The book also discusses the different metrics for energy efficiency, presents several methods and applications of the power and energy estimation, and shows how by using innovative power estimation methods and new algorithms modern processors are able to optimize metrics such as power, energy, and performance. Different power estimation tools are presented, including tools that break down the power consumption of modern processors at sub-processor core/thread granularity. The book also investigates software, firmware and hardware coordination methods of reducing power consumption, for example a compiler-assisted power management method to overcome power excursions. Lastly, it examines firmware algorithms for dynamic cache resizing and dynamic voltage and frequency scaling (DVFS) for memory sub-systems.

Author : Abderazek Ben Abdallah,Khanh N. Dang
Publisher : Springer Nature
Page : 260 pages
File Size : 43,7 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783030925253

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Neuromorphic Computing Principles and Organization by Abderazek Ben Abdallah,Khanh N. Dang Pdf

This book focuses on neuromorphic computing principles and organization and how to build fault-tolerant scalable hardware for large and medium scale spiking neural networks with learning capabilities. In addition, the book describes in a comprehensive way the organization and how to design a spike-based neuromorphic system to perform network of spiking neurons communication, computing, and adaptive learning for emerging AI applications. The book begins with an overview of neuromorphic computing systems and explores the fundamental concepts of artificial neural networks. Next, we discuss artificial neurons and how they have evolved in their representation of biological neuronal dynamics. Afterward, we discuss implementing these neural networks in neuron models, storage technologies, inter-neuron communication networks, learning, and various design approaches. Then, comes the fundamental design principle to build an efficient neuromorphic system in hardware. The challenges that need to be solved toward building a spiking neural network architecture with many synapses are discussed. Learning in neuromorphic computing systems and the major emerging memory technologies that promise neuromorphic computing are then given. A particular chapter of this book is dedicated to the circuits and architectures used for communication in neuromorphic systems. In particular, the Network-on-Chip fabric is introduced for receiving and transmitting spikes following the Address Event Representation (AER) protocol and the memory accessing method. In addition, the interconnect design principle is covered to help understand the overall concept of on-chip and off-chip communication. Advanced on-chip interconnect technologies, including si-photonic three-dimensional interconnects and fault-tolerant routing algorithms, are also given. The book also covers the main threats of reliability and discusses several recovery methods for multicore neuromorphic systems. This is important for reliable processing in several embedded neuromorphic applications. A reconfigurable design approach that supports multiple target applications via dynamic reconfigurability, network topology independence, and network expandability is also described in the subsequent chapters. The book ends with a case study about a real hardware-software design of a reliable three-dimensional digital neuromorphic processor geared explicitly toward the 3D-ICs biological brain’s three-dimensional structure. The platform enables high integration density and slight spike delay of spiking networks and features a scalable design. We present methods for fault detection and recovery in a neuromorphic system as well. Neuromorphic Computing Principles and Organization is an excellent resource for researchers, scientists, graduate students, and hardware-software engineers dealing with the ever-increasing demands on fault-tolerance, scalability, and low power consumption. It is also an excellent resource for teaching advanced undergraduate and graduate students about the fundamentals concepts, organization, and actual hardware-software design of reliable neuromorphic systems with learning and fault-tolerance capabilities.

Author : Elishai Ezra Tsur
Publisher : CRC Press
Page : 340 pages
File Size : 41,6 Mb
Release : 2021-08-26
Category : Computers
ISBN : 9781000421293

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Neuromorphic Engineering by Elishai Ezra Tsur Pdf

The brain is not a glorified digital computer. It does not store information in registers, and it does not mathematically transform mental representations to establish perception or behavior. The brain cannot be downloaded to a computer to provide immortality, nor can it destroy the world by having its emerged consciousness traveling in cyberspace. However, studying the brain's core computation architecture can inspire scientists, computer architects, and algorithm designers to think fundamentally differently about their craft. Neuromorphic engineers have the ultimate goal of realizing machines with some aspects of cognitive intelligence. They aspire to design computing architectures that could surpass existing digital von Neumann-based computing architectures' performance. In that sense, brain research bears the promise of a new computing paradigm. As part of a complete cognitive hardware and software ecosystem, neuromorphic engineering opens new frontiers for neuro-robotics, artificial intelligence, and supercomputing applications. The book presents neuromorphic engineering from three perspectives: the scientist, the computer architect, and the algorithm designer. It zooms in and out of the different disciplines, allowing readers with diverse backgrounds to understand and appreciate the field. Overall, the book covers the basics of neuronal modeling, neuromorphic circuits, neural architectures, event-based communication, and the neural engineering framework.

Author : Jingzhao Ou,Viktor K. Prasanna
Publisher : CRC Press
Page : 225 pages
File Size : 47,8 Mb
Release : 2009-10-14
Category : Computers
ISBN : 9781584887423

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Energy Efficient Hardware-Software Co-Synthesis Using Reconfigurable Hardware by Jingzhao Ou,Viktor K. Prasanna Pdf

Rapid energy estimation for energy efficient applications using field-programmable gate arrays (FPGAs) remains a challenging research topic. Energy dissipation and efficiency have prevented the widespread use of FPGA devices in embedded systems, where energy efficiency is a key performance metric. Helping overcome these challenges, Energy Efficient

Author : Vojin G. Oklobdzija,Ram K. Krishnamurthy
Publisher : Springer Science & Business Media
Page : 342 pages
File Size : 54,6 Mb
Release : 2007-04-27
Category : Technology & Engineering
ISBN : 9780387340470

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High-Performance Energy-Efficient Microprocessor Design by Vojin G. Oklobdzija,Ram K. Krishnamurthy Pdf

Written by the world’s most prominent microprocessor design leaders from industry and academia, this book provides complete coverage of all aspects of complex microprocessor design: technology, power management, clocking, high-performance architecture, design methodologies, memory and I/O design, computer aided design, testing and design for testability. The chapters provide state-of-the-art knowledge while including sufficient tutorial material to bring non-experts up to speed. A useful companion to design engineers working in related areas.

Author : Sudeep Pasricha,Muhammad Shafique
Publisher : Springer Nature
Page : 481 pages
File Size : 44,7 Mb
Release : 2023-10-09
Category : Technology & Engineering
ISBN : 9783031399329

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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing by Sudeep Pasricha,Muhammad Shafique Pdf

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 : Minghua Chen,Sid Chi-Kin Chau
Publisher : Springer Nature
Page : 85 pages
File Size : 53,7 Mb
Release : 2022-10-19
Category : Computers
ISBN : 9783031115493

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Online Capacity Provisioning for Energy-Efficient Datacenters by Minghua Chen,Sid Chi-Kin Chau Pdf

This book addresses the urgent issue of massive and inefficient energy consumption by data centers, which have become the largest co-located computing systems in the world and process trillions of megabytes of data every second. Dynamic provisioning algorithms have the potential to be the most viable and convenient of approaches to reducing data center energy consumption by turning off unnecessary servers, but they incur additional costs from being unable to properly predict future workload demands that have only recently been mitigated by advances in machine-learned predictions. This book explores whether it is possible to design effective online dynamic provisioning algorithms that require zero future workload information while still achieving close-to-optimal performance. It also examines whether characterizing the benefits of utilizing the future workload information can then improve the design of online algorithms with predictions in dynamic provisioning. The book specifically develops online dynamic provisioning algorithms with and without the available future workload information. Readers will discover the elegant structure of the online dynamic provisioning problem in a way that reveals the optimal solution through divide-and-conquer tactics. The book teaches readers to exploit this insight by showing the design of two online competitive algorithms with competitive ratios characterized by the normalized size of a look-ahead window in which exact workload prediction is available.

Author : Albert Y. Zomaya,Young Choon Lee
Publisher : John Wiley & Sons
Page : 605 pages
File Size : 54,9 Mb
Release : 2012-07-26
Category : Computers
ISBN : 9781118342008

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Energy-Efficient Distributed Computing Systems by Albert Y. Zomaya,Young Choon Lee Pdf

The energy consumption issue in distributed computing systems raises various monetary, environmental and system performance concerns. Electricity consumption in the US doubled from 2000 to 2005. From a financial and environmental standpoint, reducing the consumption of electricity is important, yet these reforms must not lead to performance degradation of the computing systems. These contradicting constraints create a suite of complex problems that need to be resolved in order to lead to 'greener' distributed computing systems. This book brings together a group of outstanding researchers that investigate the different facets of green and energy efficient distributed computing. Key features: One of the first books of its kind Features latest research findings on emerging topics by well-known scientists Valuable research for grad students, postdocs, and researchers Research will greatly feed into other technologies and application domains

Author : Somnath Paul,Swarup Bhunia
Publisher : Springer Science & Business Media
Page : 210 pages
File Size : 48,7 Mb
Release : 2013-09-07
Category : Technology & Engineering
ISBN : 9781461477983

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Computing with Memory for Energy-Efficient Robust Systems by Somnath Paul,Swarup Bhunia Pdf

This book analyzes energy and reliability as major challenges faced by designers of computing frameworks in the nanometer technology regime. The authors describe the existing solutions to address these challenges and then reveal a new reconfigurable computing platform, which leverages high-density nanoscale memory for both data storage and computation to maximize the energy-efficiency and reliability. The energy and reliability benefits of this new paradigm are illustrated and the design challenges are discussed. Various hardware and software aspects of this exciting computing paradigm are described, particularly with respect to hardware-software co-designed frameworks, where the hardware unit can be reconfigured to mimic diverse application behavior. Finally, the energy-efficiency of the paradigm described is compared with other, well-known reconfigurable computing platforms.

Author : Anup Kumar Das,Akash Kumar,Bharadwaj Veeravalli,Francky Catthoor
Publisher : Springer
Page : 147 pages
File Size : 51,5 Mb
Release : 2018-01-03
Category : Technology & Engineering
ISBN : 9783319693743

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Reliable and Energy Efficient Streaming Multiprocessor Systems by Anup Kumar Das,Akash Kumar,Bharadwaj Veeravalli,Francky Catthoor Pdf

This book discusses analysis, design and optimization techniques for streaming multiprocessor systems, while satisfying a given area, performance, and energy budget. The authors describe design flows for both application-specific and general purpose streaming systems. Coverage also includes the use of machine learning for thermal optimization at run-time, when an application is being executed. The design flow described in this book extends to thermal and energy optimization with multiple applications running sequentially and concurrently.