Artificial Intelligence Hardware Design

Artificial Intelligence Hardware Design
Author: Albert Chun-Chen Liu
Publisher: John Wiley & Sons
Total Pages: 244
Release: 2021-08-23
Genre: Computers
ISBN: 1119810477
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ARTIFICIAL INTELLIGENCE HARDWARE DESIGN Learn foundational and advanced topics in Neural Processing Unit design with real-world examples from leading voices in the field In Artificial Intelligence Hardware Design: Challenges and Solutions, distinguished researchers and authors Drs. Albert Chun Chen Liu and Oscar Ming Kin Law deliver a rigorous and practical treatment of the design applications of specific circuits and systems for accelerating neural network processing. Beginning with a discussion and explanation of neural networks and their developmental history, the book goes on to describe parallel architectures, streaming graphs for massive parallel computation, and convolution optimization. The authors offer readers an illustration of in-memory computation through Georgia Tech’s Neurocube and Stanford’s Tetris accelerator using the Hybrid Memory Cube, as well as near-memory architecture through the embedded eDRAM of the Institute of Computing Technology, the Chinese Academy of Science, and other institutions. Readers will also find a discussion of 3D neural processing techniques to support multiple layer neural networks, as well as information like: A thorough introduction to neural networks and neural network development history, as well as Convolutional Neural Network (CNN) models Explorations of various parallel architectures, including the Intel CPU, Nvidia GPU, Google TPU, and Microsoft NPU, emphasizing hardware and software integration for performance improvement Discussions of streaming graph for massive parallel computation with the Blaize GSP and Graphcore IPU An examination of how to optimize convolution with UCLA Deep Convolutional Neural Network accelerator filter decomposition Perfect for hardware and software engineers and firmware developers, Artificial Intelligence Hardware Design is an indispensable resource for anyone working with Neural Processing Units in either a hardware or software capacity.

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning
Author: Shiho Kim
Publisher: Elsevier
Total Pages: 414
Release: 2021-04-07
Genre: Computers
ISBN: 0128231238
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Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more. Updates on new information on the architecture of GPU, NPU and DNN Discusses In-memory computing, Machine intelligence and Quantum computing Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance

Artificial Intelligence and Hardware Accelerators

Artificial Intelligence and Hardware Accelerators
Author: Ashutosh Mishra
Publisher: Springer Nature
Total Pages: 358
Release: 2023-03-15
Genre: Technology & Engineering
ISBN: 3031221702
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This book explores new methods, architectures, tools, and algorithms for Artificial Intelligence Hardware Accelerators. The authors have structured the material to simplify readers’ journey toward understanding the aspects of designing hardware accelerators, complex AI algorithms, and their computational requirements, along with the multifaceted applications. Coverage focuses broadly on the hardware aspects of training, inference, mobile devices, and autonomous vehicles (AVs) based AI accelerators

Hardware for Artificial Intelligence

Hardware for Artificial Intelligence
Author: Alexantrou Serb
Publisher: Frontiers Media SA
Total Pages: 229
Release: 2022-09-26
Genre: Science
ISBN: 2889763986
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Hardware-Aware Probabilistic Machine Learning Models

Hardware-Aware Probabilistic Machine Learning Models
Author: Laura Isabel Galindez Olascoaga
Publisher: Springer Nature
Total Pages: 163
Release: 2021-05-19
Genre: Technology & Engineering
ISBN: 3030740420
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This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and performance of the machine learning task, with the overarching goal of balancing the two optimally. The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover. The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering.

VLSI and Hardware Implementations using Modern Machine Learning Methods

VLSI and Hardware Implementations using Modern Machine Learning Methods
Author: Sandeep Saini
Publisher: CRC Press
Total Pages: 329
Release: 2021-12-30
Genre: Technology & Engineering
ISBN: 1000523810
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Machine learning is a potential solution to resolve bottleneck issues in VLSI via optimizing tasks in the design process. This book aims to provide the latest machine-learning–based methods, algorithms, architectures, and frameworks designed for VLSI design. The focus is on digital, analog, and mixed-signal design techniques, device modeling, physical design, hardware implementation, testability, reconfigurable design, synthesis and verification, and related areas. Chapters include case studies as well as novel research ideas in the given field. Overall, the book provides practical implementations of VLSI design, IC design, and hardware realization using machine learning techniques. Features: Provides the details of state-of-the-art machine learning methods used in VLSI design Discusses hardware implementation and device modeling pertaining to machine learning algorithms Explores machine learning for various VLSI architectures and reconfigurable computing Illustrates the latest techniques for device size and feature optimization Highlights the latest case studies and reviews of the methods used for hardware implementation This book is aimed at researchers, professionals, and graduate students in VLSI, machine learning, electrical and electronic engineering, computer engineering, and hardware systems.

Efficient Processing of Deep Neural Networks

Efficient Processing of Deep Neural Networks
Author: Vivienne Sze
Publisher: Springer Nature
Total Pages: 254
Release: 2022-05-31
Genre: Technology & Engineering
ISBN: 3031017668
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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.

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning
Author:
Publisher: Academic Press
Total Pages: 416
Release: 2021-03-28
Genre: Computers
ISBN: 0128231246
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Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more. Updates on new information on the architecture of GPU, NPU and DNN Discusses In-memory computing, Machine intelligence and Quantum computing Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance

VLSI for Artificial Intelligence and Neural Networks

VLSI for Artificial Intelligence and Neural Networks
Author: Jose G. Delgado-Frias
Publisher: Springer Science & Business Media
Total Pages: 411
Release: 2012-12-06
Genre: Computers
ISBN: 1461537525
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This book is an edited selection of the papers presented at the International Workshop on VLSI for Artifidal Intelligence and Neural Networks which was held at the University of Oxford in September 1990. Our thanks go to all the contributors and especially to the programme committee for all their hard work. Thanks are also due to the ACM-SIGARCH, the IEEE Computer Society, and the lEE for publicizing the event and to the University of Oxford and SUNY-Binghamton for their active support. We are particularly grateful to Anna Morris, Maureen Doherty and Laura Duffy for coping with the administrative problems. Jose Delgado-Frias Will Moore April 1991 vii PROLOGUE Artificial intelligence and neural network algorithms/computing have increased in complexity as well as in the number of applications. This in tum has posed a tremendous need for a larger computational power than can be provided by conventional scalar processors which are oriented towards numeric and data manipulations. Due to the artificial intelligence requirements (symbolic manipulation, knowledge representation, non-deterministic computations and dynamic resource allocation) and neural network computing approach (non-programming and learning), a different set of constraints and demands are imposed on the computer architectures for these applications.

Mastering AI Hardware

Mastering AI Hardware
Author: Rosey Press
Publisher: Independently Published
Total Pages: 0
Release: 2024-04-29
Genre: Computers
ISBN:
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Understanding AI Hardware In the subchapter "Understanding AI Hardware," we delve into the intricate world of artificial intelligence processors and the essential components that make up these advanced systems. For those seeking a comprehensive guide to AI hardware, this section provides a detailed comparison of various neural network processor architectures, shedding light on their unique features and capabilities. By understanding the differences between these architectures, readers can make informed decisions when selecting the most suitable hardware for their AI projects. Moreover, this subchapter offers an in-depth analysis of the hardware requirements for training AI models, highlighting the key factors that impact performance and efficiency. From processing power to memory bandwidth, each component plays a crucial role in accelerating the training process and optimizing model accuracy. By mastering these hardware requirements, readers can enhance the speed and accuracy of their AI models, leading to more effective outcomes in various applications such as image recognition and natural language processing. Furthermore, this section provides a guide to optimizing AI hardware for specific use cases, offering insights into the strategies and techniques that can enhance performance and efficiency. Whether it's fine-tuning hardware configurations or leveraging specialized processors, readers will learn how to tailor their hardware setups to meet the unique demands of different AI applications. By optimizing AI hardware, individuals can achieve superior performance and efficiency, unlocking new possibilities in the field of artificial intelligence. Additionally, this subchapter reviews the latest advancements in AI hardware technology, exploring the cutting-edge innovations that are shaping the future of computing. From novel processor architectures to breakthroughs in hardware design, readers will gain valuable insights into the evolving landscape of AI hardware. By staying informed about the latest developments, individuals can stay ahead of the curve and leverage the most advanced hardware solutions for their AI projects. In conclusion, "Understanding AI Hardware" offers a comprehensive overview of the components and functions of neural network processors, shedding light on their critical role in powering artificial intelligence applications. By exploring the impact of AI hardware on the future of computing, readers can gain a deeper understanding of the transformative potential of these advanced systems. Whether building custom AI hardware solutions or navigating the challenges and limitations of current technology, this subchapter equips individuals with the knowledge and insights needed to excel in the dynamic field of AI hardware.