Artificial Intelligence And Hardware Accelerators

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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

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.

Artificial Intelligence And Hardware Accelerators

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Author : Ashutosh Mishra
language : en
Publisher:
Release Date : 2023

Artificial Intelligence And Hardware Accelerators written by Ashutosh Mishra and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with 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.

Fpga Based Hardware Accelerators

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Author : Iouliia Skliarova
language : en
Publisher: Springer
Release Date : 2019-05-30

Fpga Based Hardware Accelerators written by Iouliia Skliarova and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-30 with Technology & Engineering categories.

This book suggests and describes a number of fast parallel circuits for data/vector processing using FPGA-based hardware accelerators. Three primary areas are covered: searching, sorting, and counting in combinational and iterative networks. These include the application of traditional structures that rely on comparators/swappers as well as alternative networks with a variety of core elements such as adders, logical gates, and look-up tables. The iterative technique discussed in the book enables the sequential reuse of relatively large combinational blocks that execute many parallel operations with small propagation delays. For each type of network discussed, the main focus is on the step-by-step development of the architectures proposed from initial concepts to synthesizable hardware description language specifications. Each type of network is taken through several stages, including modeling the desired functionality in software, the retrieval and automatic conversion of key functions, leading to specifications for optimized hardware modules. The resulting specifications are then synthesized, implemented, and tested in FPGAs using commercial design environments and prototyping boards. The methods proposed can be used in a range of data processing applications, including traditional sorting, the extraction of maximum and minimum subsets from large data sets, communication-time data processing, finding frequently occurring items in a set, and Hamming weight/distance counters/comparators. The book is intended to be a valuable support material for university and industrial engineering courses that involve FPGA-based circuit and system design.

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.

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.

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

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.

Tinyml

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Author : Pete Warden
language : en
Publisher: O'Reilly Media
Release Date : 2019-12-16

Tinyml written by Pete Warden and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-16 with Computers categories.

Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size

Machine Learning

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Author : Hamed Farhadi
language : en
Publisher: BoD – Books on Demand
Release Date : 2018-09-19

Machine Learning written by Hamed Farhadi and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-19 with Computers categories.

The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled real-time collection and processing of the generated data across different sections. On the one hand, the internet of things (IoT) enabled by cellular telecommunication industry connects various types of sensors that can collect heterogeneous data. On the other hand, the recent advances in computational capabilities such as parallel processing in graphical processing units (GPUs) and distributed processing over cloud computing clusters enabled the processing of a vast amount of data. There has been a vital need to discover important patterns and infer trends from a large volume of data (so-called Big Data) to empower data-driven decision-making processes. Tools and techniques have been developed in machine learning to draw insightful conclusions from available data in a structured and automated fashion. Machine learning algorithms are based on concepts and tools developed in several fields including statistics, artificial intelligence, information theory, cognitive science, and control theory. The recent advances in machine learning have had a broad range of applications in different scientific disciplines. This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses.