Hardware For Artificial Intelligence
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Hardware For Artificial Intelligence
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Author : Alexantrou Serb
language : en
Publisher: Frontiers Media SA
Release Date : 2022-09-26
Hardware For Artificial Intelligence written by Alexantrou Serb and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-26 with Science categories.
Hardware Aware Probabilistic Machine Learning Models
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Author : Laura Isabel Galindez Olascoaga
language : en
Publisher: Springer Nature
Release Date : 2021-05-19
Hardware Aware Probabilistic Machine Learning Models written by Laura Isabel Galindez Olascoaga and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-19 with Technology & Engineering categories.
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.
Artificial Intelligence Hardware Design
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Author : Albert Chun-Chen Liu
language : en
Publisher: John Wiley & Sons
Release Date : 2021-08-31
Artificial Intelligence Hardware Design written by Albert Chun-Chen Liu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-31 with Computers categories.
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.
Efficient Processing Of Deep Neural Networks
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Author : Vivienne Sze
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2020-06-24
Efficient Processing Of Deep Neural Networks written by Vivienne Sze and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-24 with Computers categories.
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 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 the 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 a formalization and organization of key concepts from contemporary works that provides insights that may spark new ideas.
Artificial Intelligence And Hardware Accelerators
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Author : Ashutosh Mishra
language : en
Publisher: Springer Nature
Release Date : 2023-03-15
Artificial Intelligence And Hardware Accelerators written by Ashutosh Mishra and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-15 with Technology & Engineering categories.
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
Vlsi And Hardware Implementations Using Modern Machine Learning Methods
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Author : Sandeep Saini
language : en
Publisher: CRC Press
Release Date : 2021-12-30
Vlsi And Hardware Implementations Using Modern Machine Learning Methods written by Sandeep Saini and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-30 with Technology & Engineering categories.
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.
Hardware Accelerator Systems For Artificial Intelligence And Machine Learning
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Author :
language : en
Publisher: Academic Press
Release Date : 2021-03-28
Hardware Accelerator Systems For Artificial Intelligence And Machine Learning written by and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-28 with Mathematics categories.
Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into artificial 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
Hardware Architectures For Deep Learning
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Author : Masoud Daneshtalab
language : en
Publisher: Institution of Engineering and Technology
Release Date : 2020-02-28
Hardware Architectures For Deep Learning written by Masoud Daneshtalab and has been published by Institution of Engineering and Technology this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-28 with Computers categories.
This book presents and discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks.
Ai Focused Hardware
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Author : Kai Turing
language : en
Publisher: Publifye AS
Release Date : 2025-01-06
Ai Focused Hardware written by Kai Turing and has been published by Publifye AS this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-06 with Computers categories.
'AI-Focused Hardware' presents a comprehensive exploration of specialized hardware architectures driving modern artificial intelligence systems. The book masterfully bridges the gap between traditional computing limitations and the demanding requirements of AI applications by examining three crucial areas: neural processing units (NPUs), AI-optimized memory architectures, and quantum computing implementations for machine learning. Through a well-structured progression, the text begins with the historical evolution from general-purpose processors to specialized AI hardware, establishing a foundation for understanding current innovations. The book's unique value lies in its practical approach, offering detailed schematics and architecture diagrams that practitioners can directly implement. Notable insights include the crucial role of processing-in-memory systems in overcoming traditional memory bottlenecks and the practical applications of tensor processing units in modern AI workloads. The content maintains accessibility while delving into complex technical concepts, making it valuable for both hardware engineers and AI practitioners. Each section builds upon the previous, moving from fundamental NPU design principles through advanced memory hierarchies, and culminating in practical quantum computing applications. The inclusion of real-world implementation cases, performance metrics, and comparative analyses from major hardware manufacturers provides readers with concrete evidence supporting the book's central argument that purpose-built hardware architectures are essential for advancing AI capabilities.
Artificial Intelligence Hardware Design
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Author : Albert Chun-Chen Liu
language : en
Publisher: John Wiley & Sons
Release Date : 2021-08-23
Artificial Intelligence Hardware Design written by Albert Chun-Chen Liu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-23 with Computers categories.
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.