Artificial Intelligence Hardware Design

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Artificial Intelligence Hardware Design

Albert Chun-Chen Liu,Oscar Ming Kin Law
Artificial Intelligence Hardware Design Book PDF

Author : Albert Chun-Chen Liu,Oscar Ming Kin Law
Publisher : John Wiley & Sons
Page : 244 pages
File Size : 47,6 Mb
Release : 2021-08-23
Category : Computers
ISBN : 9781119810476

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Artificial Intelligence Hardware Design by Albert Chun-Chen Liu,Oscar Ming Kin Law Pdf

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

Ashutosh Mishra,Jaekwang Cha,Hyunbin Park,Shiho Kim
Artificial Intelligence and Hardware Accelerators Book PDF

Author : Ashutosh Mishra,Jaekwang Cha,Hyunbin Park,Shiho Kim
Publisher : Springer Nature
Page : 358 pages
File Size : 40,9 Mb
Release : 2023-03-15
Category : Technology & Engineering
ISBN : 9783031221705

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Artificial Intelligence and Hardware Accelerators by Ashutosh Mishra,Jaekwang Cha,Hyunbin Park,Shiho Kim Pdf

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 Accelerator Systems for Artificial Intelligence and Machine Learning

Shiho Kim,Ganesh Chandra Deka
Hardware Accelerator Systems for Artificial Intelligence and Machine Learning Book PDF

Author : Shiho Kim,Ganesh Chandra Deka
Publisher : Elsevier
Page : 414 pages
File Size : 41,8 Mb
Release : 2021-04-07
Category : Computers
ISBN : 9780128231234

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Hardware Accelerator Systems for Artificial Intelligence and Machine Learning by Shiho Kim,Ganesh Chandra Deka Pdf

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

Efficient Processing of Deep Neural Networks

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

Author : Vivienne Sze,Yu-Hsin Chen,Tien-Ju Yang,Joel S. Emer
Publisher : Springer Nature
Page : 254 pages
File Size : 42,8 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.

VLSI and Hardware Implementations using Modern Machine Learning Methods

Sandeep Saini,Kusum Lata,G.R. Sinha
VLSI and Hardware Implementations using Modern Machine Learning Methods Book PDF

Author : Sandeep Saini,Kusum Lata,G.R. Sinha
Publisher : CRC Press
Page : 329 pages
File Size : 41,5 Mb
Release : 2021-12-30
Category : Technology & Engineering
ISBN : 9781000523812

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VLSI and Hardware Implementations using Modern Machine Learning Methods by Sandeep Saini,Kusum Lata,G.R. Sinha Pdf

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. Illustrate latest techniques for device size and feature optimization. Highlight latest case studies and reviews of the methods used for hardware implementation.

The Dictionary of Artificial Intelligence

Utku Taşova
The Dictionary of Artificial Intelligence Book PDF

Author : Utku Taşova
Publisher : Entropol
Page : 565 pages
File Size : 48,7 Mb
Release : 2023-11-03
Category : Computers
ISBN : 8210379456XXX

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The Dictionary of Artificial Intelligence by Utku Taşova Pdf

Unveiling the Future: Your Portal to Artificial Intelligence Proficiency In the epoch of digital metamorphosis, Artificial Intelligence (AI) stands as the vanguard of a new dawn, a nexus where human ingenuity intertwines with machine precision. As we delve deeper into this uncharted realm, the boundary between the conceivable and the fantastical continually blurs, heralding a new era of endless possibilities. The Dictionary of Artificial Intelligence, embracing a compendium of 3,300 meticulously curated titles, endeavors to be the torchbearer in this journey of discovery, offering a wellspring of knowledge to both the uninitiated and the adept. Embarking on the pages of this dictionary is akin to embarking on a voyage through the vast and often turbulent seas of AI. Each entry serves as a beacon, illuminating complex terminologies, core principles, and the avant-garde advancements that characterize this dynamic domain. The dictionary is more than a mere compilation of terms; it's a labyrinth of understanding waiting to be traversed. The Dictionary of Artificial Intelligence is an endeavor to demystify the arcane, to foster a shared lexicon that enhances collaboration, innovation, and comprehension across the AI community. It's a mission to bridge the chasm between ignorance and insight, to unravel the intricacies of AI that often seem enigmatic to the outsiders. This profound reference material transcends being a passive repository of terms; it’s an engagement with the multifaceted domain of artificial intelligence. Each title encapsulated within these pages is a testament to the audacity of human curiosity and the unyielding quest for advancement that propels the AI domain forward. The Dictionary of Artificial Intelligence is an invitation to delve deeper, to grapple with the lexicon of a field that stands at the cusp of redefining the very fabric of society. It's a conduit through which the curious become enlightened, the proficient become masters, and the innovators find inspiration. As you traverse through the entries of The Dictionary of Artificial Intelligence, you are embarking on a journey of discovery. A journey that not only augments your understanding but also ignites the spark of curiosity and the drive for innovation that are quintessential in navigating the realms of AI. We beckon you to commence this educational expedition, to explore the breadth and depth of AI lexicon, and to emerge with a boundless understanding and an unyielding resolve to contribute to the ever-evolving narrative of artificial intelligence. Through The Dictionary of Artificial Intelligence, may your quest for knowledge be as boundless and exhilarating as the domain it explores.

AI for Computer Architecture

Lizhong Chen,Drew Penney,Daniel Jiménez
AI for Computer Architecture Book PDF

Author : Lizhong Chen,Drew Penney,Daniel Jiménez
Publisher : Springer Nature
Page : 124 pages
File Size : 40,5 Mb
Release : 2022-05-31
Category : Technology & Engineering
ISBN : 9783031017704

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AI for Computer Architecture by Lizhong Chen,Drew Penney,Daniel Jiménez Pdf

Artificial intelligence has already enabled pivotal advances in diverse fields, yet its impact on computer architecture has only just begun. In particular, recent work has explored broader application to the design, optimization, and simulation of computer architecture. Notably, machine-learning-based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches. This book reviews the application of machine learning in system-wide simulation and run-time optimization, and in many individual components such as caches/memories, branch predictors, networks-on-chip, and GPUs. The book further analyzes current practice to highlight useful design strategies and identify areas for future work, based on optimized implementation strategies, opportune extensions to existing work, and ambitious long term possibilities. Taken together, these strategies and techniques present a promising future for increasingly automated computer architecture designs.

Hardware-Aware Probabilistic Machine Learning Models

Laura Isabel Galindez Olascoaga,Wannes Meert,Marian Verhelst
Hardware Aware Probabilistic Machine Learning Models Book PDF

Author : Laura Isabel Galindez Olascoaga,Wannes Meert,Marian Verhelst
Publisher : Springer Nature
Page : 163 pages
File Size : 43,7 Mb
Release : 2021-05-19
Category : Technology & Engineering
ISBN : 9783030740429

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Hardware-Aware Probabilistic Machine Learning Models by Laura Isabel Galindez Olascoaga,Wannes Meert,Marian Verhelst Pdf

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.

Mastering AI Hardware

Rosey Press
Mastering AI Hardware Book PDF

Author : Rosey Press
Publisher : Independently Published
Page : 0 pages
File Size : 42,5 Mb
Release : 2024-04-29
Category : Computers
ISBN : 9798324307837

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Mastering AI Hardware by Rosey Press Pdf

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.

VLSI and Hardware Implementations using Modern Machine Learning Methods

Sandeep Saini,Kusum Lata,G.R. Sinha
VLSI and Hardware Implementations using Modern Machine Learning Methods Book PDF

Author : Sandeep Saini,Kusum Lata,G.R. Sinha
Publisher : CRC Press
Page : 292 pages
File Size : 44,8 Mb
Release : 2021-12-31
Category : Technology & Engineering
ISBN : 9781000523843

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VLSI and Hardware Implementations using Modern Machine Learning Methods by Sandeep Saini,Kusum Lata,G.R. Sinha Pdf

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.

Embedded Artificial Intelligence

Ovidiu Vermesan,Mario Diaz Nava,Björn Debaillie
Embedded Artificial Intelligence Book PDF

Author : Ovidiu Vermesan,Mario Diaz Nava,Björn Debaillie
Publisher : CRC Press
Page : 143 pages
File Size : 48,7 Mb
Release : 2023-05-05
Category : Computers
ISBN : 9781000881912

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Embedded Artificial Intelligence by Ovidiu Vermesan,Mario Diaz Nava,Björn Debaillie Pdf

Recent technological developments in sensors, edge computing, connectivity, and artificial intelligence (AI) technologies have accelerated the integration of data analysis based on embedded AI capabilities into resource-constrained, energy-efficient hardware devices for processing information at the network edge. Embedded AI combines embedded machine learning (ML) and deep learning (DL) based on neural networks (NN) architectures such as convolutional NN (CNN), or spiking neural network (SNN) and algorithms on edge devices and implements edge computing capabilities that enable data processing and analysis without optimised connectivity and integration, allowing users to access data from various sources. Embedded AI efficiently implements edge computing and AI processes on resource-constrained devices to mitigate downtime and service latency, and it successfully merges AI processes as a pivotal component in edge computing and embedded system devices. Embedded AI also enables users to reduce costs, communication, and processing time by assembling data and by supporting user requirements without the need for continuous interaction with physical locations. This book provides an overview of the latest research results and activities in industrial embedded AI technologies and applications, based on close cooperation between three large-scale ECSEL JU projects, AI4DI, ANDANTE, and TEMPO. The book’s content targets researchers, designers, developers, academics, post-graduate students and practitioners seeking recent research on embedded AI. It combines the latest developments in embedded AI, addressing methodologies, tools, and techniques to offer insight into technological trends and their use across different industries.

Deep Learning on Edge Computing Devices

Xichuan Zhou,Haijun Liu,Cong Shi,Ji Liu
Deep Learning on Edge Computing Devices Book PDF

Author : Xichuan Zhou,Haijun Liu,Cong Shi,Ji Liu
Publisher : Elsevier
Page : 200 pages
File Size : 49,9 Mb
Release : 2022-02-02
Category : Computers
ISBN : 9780323909273

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Deep Learning on Edge Computing Devices by Xichuan Zhou,Haijun Liu,Cong Shi,Ji Liu Pdf

Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks. The title helps researchers maximize the performance of Edge-deep learning models for mobile computing and other applications by presenting neural network algorithms and hardware design optimization approaches for Edge-deep learning. Applications are introduced in each section, and a comprehensive example, smart surveillance cameras, is presented at the end of the book, integrating innovation in both algorithm and hardware architecture. Structured into three parts, the book covers core concepts, theories and algorithms and architecture optimization. This book provides a solution for researchers looking to maximize the performance of deep learning models on Edge-computing devices through algorithm-hardware co-design. Focuses on hardware architecture and embedded deep learning, including neural networks Brings together neural network algorithm and hardware design optimization approaches to deep learning, alongside real-world applications Considers how Edge computing solves privacy, latency and power consumption concerns related to the use of the Cloud Describes how to maximize the performance of deep learning on Edge-computing devices Presents the latest research on neural network compression coding, deep learning algorithms, chip co-design and intelligent monitoring

VLSI for Artificial Intelligence and Neural Networks

Jose G. Delgado-Frias,W.R. Moore
VLSI for Artificial Intelligence and Neural Networks Book PDF

Author : Jose G. Delgado-Frias,W.R. Moore
Publisher : Springer Science & Business Media
Page : 411 pages
File Size : 43,6 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461537526

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VLSI for Artificial Intelligence and Neural Networks by Jose G. Delgado-Frias,W.R. Moore Pdf

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.

Mobile Edge Artificial Intelligence

Yuanming Shi,Kai Yang,Zhanpeng Yang,Yong Zhou
Mobile Edge Artificial Intelligence Book PDF

Author : Yuanming Shi,Kai Yang,Zhanpeng Yang,Yong Zhou
Publisher : Academic Press
Page : 208 pages
File Size : 55,8 Mb
Release : 2021-08-07
Category : Computers
ISBN : 9780128238356

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Mobile Edge Artificial Intelligence by Yuanming Shi,Kai Yang,Zhanpeng Yang,Yong Zhou Pdf

Mobile Edge Artificial Intelligence: Opportunities and Challenges presents recent advances in wireless technologies and nonconvex optimization techniques for designing efficient edge AI systems. The book includes comprehensive coverage on modeling, algorithm design and theoretical analysis. Through typical examples, the powerfulness of this set of systems and algorithms is demonstrated, along with their abilities to make low-latency, reliable and private intelligent decisions at network edge. With the availability of massive datasets, high performance computing platforms, sophisticated algorithms and software toolkits, AI has achieved remarkable success in many application domains. As such, intelligent wireless networks will be designed to leverage advanced wireless communications and mobile computing technologies to support AI-enabled applications at various edge mobile devices with limited communication, computation, hardware and energy resources. Presents advanced key enabling techniques, including model compression, wireless MapReduce and wireless cooperative transmission Provides advanced 6G wireless techniques, including over-the-air computation and reconfigurable intelligent surface Includes principles for designing communication-efficient edge inference systems, communication-efficient training systems, and communication-efficient optimization algorithms for edge machine learning

Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing

Sudeep Pasricha,Muhammad Shafique
Embedded Machine Learning for Cyber Physical IoT and Edge Computing Book PDF

Author : Sudeep Pasricha,Muhammad Shafique
Publisher : Springer Nature
Page : 418 pages
File Size : 50,7 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.