Deep Learning Hardware Design

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Deep Learning: Hardware Design

Author : Albert Liu Oscar Law
Publisher :
ISBN 13 :
Total Pages : 251 pages
Book Rating : 4.6/5 (682 download)

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Book Synopsis Deep Learning: Hardware Design by : Albert Liu Oscar Law

Download or read book Deep Learning: Hardware Design written by Albert Liu Oscar Law and published by . This book was released on 2020-07-21 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: 2nd edition. With the Convolutional Neural Network (CNN) breakthrough in 2012, the deep learning is widely appliedto our daily life, automotive, retail, healthcare and finance. In 2016, Alpha Go with ReinforcementLearning (RL) further proves new Artificial Intelligent (AI) revolution gradually changes our society, likepersonal computer (1977), internet (1994) and smartphone (2007) before. However, most of effortfocuses on software development and seldom addresses the hardware challenges:* Big input data* Deep neural network* Massive parallel processing* Reconfigurable network* Memory bottleneck* Intensive computation* Network pruning* Data sparsityThis book reviews various hardware designs range from CPU, GPU to NPU and list out special features toresolve above problems. New hardware can be evolved from those designs for performance and powerimprovement* Parallel architecture* Convolution optimization* In-memory computation* Near-memory architecture* Network optimizationOrganization of the Book1. Chapter 1 introduces neural network and discuss neural network development history2. Chapter 2 reviews Convolutional Neural Network model and describes each layer function and itsexample3. Chapter 3 list out several parallel architectures, Intel CPU, Nvidia GPU, Google TPU and MicrosoftNPU4. Chapter 4 highlights how to optimize convolution with UCLA DCNN accelerator and MIT EyerissDNN accelerator as example5. Chapter 5 illustrates GT Neurocube architecture and Stanford Tetris DNN process with in-memorycomputation using Hybrid Memory Cube (HMC)6. Chapter 6 proposes near-memory architecture with ICT DaDianNao supercomputer and UofTCnvlutin DNN accelerator7. Chapter 7 chooses energy efficient inference engine for network pruning3We continue to study new approaches to enhance deep learning hardware designs and several topics willbe incorporated into future revision* Distributive graph theory* High speed arithmetic* 3D neural processing

Artificial Intelligence Hardware Design

Author : Albert Chun-Chen Liu
Publisher : John Wiley & Sons
ISBN 13 : 1119810477
Total Pages : 244 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Artificial Intelligence Hardware Design by : Albert Chun-Chen Liu

Download or read book Artificial Intelligence Hardware Design written by Albert Chun-Chen Liu and published by John Wiley & Sons. This book was released on 2021-08-23 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : Vivienne Sze
Publisher : Springer Nature
ISBN 13 : 3031017668
Total Pages : 254 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Efficient Processing of Deep Neural Networks by : Vivienne Sze

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Architectures for Deep Learning

Author : Masoud Daneshtalab
Publisher : Institution of Engineering and Technology
ISBN 13 : 1785617680
Total Pages : 329 pages
Book Rating : 4.7/5 (856 download)

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Book Synopsis Hardware Architectures for Deep Learning by : Masoud Daneshtalab

Download or read book Hardware Architectures for Deep Learning written by Masoud Daneshtalab and published by Institution of Engineering and Technology. This book was released on 2020-04-24 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents and discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks. The rapid growth of server, desktop, and embedded applications based on deep learning has brought about a renaissance in interest in neural networks, with applications including image and speech processing, data analytics, robotics, healthcare monitoring, and IoT solutions. Efficient implementation of neural networks to support complex deep learning-based applications is a complex challenge for embedded and mobile computing platforms with limited computational/storage resources and a tight power budget. Even for cloud-scale systems it is critical to select the right hardware configuration based on the neural network complexity and system constraints in order to increase power- and performance-efficiency. Hardware Architectures for Deep Learning provides an overview of this new field, from principles to applications, for researchers, postgraduate students and engineers who work on learning-based services and hardware platforms.

VLSI and Hardware Implementations using Modern Machine Learning Methods

Author : Sandeep Saini
Publisher : CRC Press
ISBN 13 : 1000523810
Total Pages : 329 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis VLSI and Hardware Implementations using Modern Machine Learning Methods by : Sandeep Saini

Download or read book VLSI and Hardware Implementations using Modern Machine Learning Methods written by Sandeep Saini and published by CRC Press. This book was released on 2021-12-30 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : Shiho Kim
Publisher : Elsevier
ISBN 13 : 0128231238
Total Pages : 414 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Hardware Accelerator Systems for Artificial Intelligence and Machine Learning by : Shiho Kim

Download or read book Hardware Accelerator Systems for Artificial Intelligence and Machine Learning written by Shiho Kim and published by Elsevier. This book was released on 2021-04-07 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Deep Learning

Author : Albert Liu Oscar Law
Publisher :
ISBN 13 :
Total Pages : 252 pages
Book Rating : 4.6/5 (23 download)

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Book Synopsis Deep Learning by : Albert Liu Oscar Law

Download or read book Deep Learning written by Albert Liu Oscar Law and published by . This book was released on 2020-03-09 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: Second Edition.With the Convolutional Neural Network (CNN) breakthrough in 2012, the deep learning is widely appliedto our daily life, automotive, retail, healthcare and finance. In 2016, Alpha Go with ReinforcementLearning (RL) further proves new Artificial Intelligent (AI) revolution gradually changes our society, likepersonal computer (1977), internet (1994) and smartphone (2007) before. However, most of effortfocuses on software development and seldom addresses the hardware challenges: - Big input data- Deep neural network- Massive parallel processing- Reconfigurable network- Memory bottleneck- Intensive computation- Network pruning- Data sparsityThis book reviews various hardware designs range from CPU, GPU to NPU and list out special features toresolve above problems. New hardware can be evolved from those designs for performance and powerimprovement- Parallel architecture- Convolution optimization- In-memory computation- Near-memory architecture- Network optimizationOrganization of the Book1. Chapter 1 introduces neural network and discuss neural network development history2. Chapter 2 reviews Convolutional Neural Network model and describes each layer function and itsexample3. Chapter 3 list out several parallel architectures, Intel CPU, Nvidia GPU, Google TPU and MicrosoftNPU4. Chapter 4 highlights how to optimize convolution with UCLA DCNN accelerator and MIT EyerissDNN accelerator as example5. Chapter 5 illustrates GT Neurocube architecture and Stanford Tetris DNN process with in-memorycomputation using Hybrid Memory Cube (HMC)6. Chapter 6 proposes near-memory architecture with ICT DaDianNao supercomputer and UofTCnvlutin DNN accelerator7. Chapter 7 chooses energy efficient inference engine for network pruning3We continue to study new approaches to enhance deep learning hardware designs and several topics willbe incorporated into future revision- Distributive graph theory- High speed arithmetic- 3D neural processing

Deep Learning Systems

Author : Andres Rodriguez
Publisher : Springer Nature
ISBN 13 : 3031017692
Total Pages : 245 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Deep Learning Systems by : Andres Rodriguez

Download or read book Deep Learning Systems written by Andres Rodriguez and published by Springer Nature. This book was released on 2022-05-31 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets. The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to better collaborate with engineers working in other parts of the system stack. The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets. Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.

Deep Learning for Computer Architects

Author : Brandon Reagen
Publisher : Springer Nature
ISBN 13 : 3031017560
Total Pages : 109 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Deep Learning for Computer Architects by : Brandon Reagen

Download or read book Deep Learning for Computer Architects written by Brandon Reagen and published by Springer Nature. This book was released on 2022-05-31 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.

Thinking Machines

Author : Shigeyuki Takano
Publisher : Academic Press
ISBN 13 : 0128182806
Total Pages : 324 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Thinking Machines by : Shigeyuki Takano

Download or read book Thinking Machines written by Shigeyuki Takano and published by Academic Press. This book was released on 2021-03-27 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Thinking Machines: Machine Learning and Its Hardware Implementation covers the theory and application of machine learning, neuromorphic computing and neural networks. This is the first book that focuses on machine learning accelerators and hardware development for machine learning. It presents not only a summary of the latest trends and examples of machine learning hardware and basic knowledge of machine learning in general, but also the main issues involved in its implementation. Readers will learn what is required for the design of machine learning hardware for neuromorphic computing and/or neural networks. This is a recommended book for those who have basic knowledge of machine learning or those who want to learn more about the current trends of machine learning. Presents a clear understanding of various available machine learning hardware accelerator solutions that can be applied to selected machine learning algorithms Offers key insights into the development of hardware, from algorithms, software, logic circuits, to hardware accelerators Introduces the baseline characteristics of deep neural network models that should be treated by hardware as well Presents readers with a thorough review of past research and products, explaining how to design through ASIC and FPGA approaches for target machine learning models Surveys current trends and models in neuromorphic computing and neural network hardware architectures Outlines the strategy for advanced hardware development through the example of deep learning accelerators

Embedded Deep Learning

Author : Bert Moons
Publisher : Springer
ISBN 13 : 3319992236
Total Pages : 206 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Embedded Deep Learning by : Bert Moons

Download or read book Embedded Deep Learning written by Bert Moons and published by Springer. This book was released on 2018-10-23 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning. Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices; Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy – applications, algorithms, hardware architectures, and circuits – supported by real silicon prototypes; Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations; Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization’s implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

Author :
Publisher : Academic Press
ISBN 13 : 0128231246
Total Pages : 416 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Hardware Accelerator Systems for Artificial Intelligence and Machine Learning by :

Download or read book Hardware Accelerator Systems for Artificial Intelligence and Machine Learning written by and published by Academic Press. This book was released on 2021-03-28 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Deep Learning on Edge Computing Devices

Author : Xichuan Zhou
Publisher : Elsevier
ISBN 13 : 0323909272
Total Pages : 200 pages
Book Rating : 4.3/5 (239 download)

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Book Synopsis Deep Learning on Edge Computing Devices by : Xichuan Zhou

Download or read book Deep Learning on Edge Computing Devices written by Xichuan Zhou and published by Elsevier. This book was released on 2022-02-02 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Compact and Fast Machine Learning Accelerator for IoT Devices

Author : Hantao Huang
Publisher : Springer
ISBN 13 : 9811333238
Total Pages : 149 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Compact and Fast Machine Learning Accelerator for IoT Devices by : Hantao Huang

Download or read book Compact and Fast Machine Learning Accelerator for IoT Devices written by Hantao Huang and published by Springer. This book was released on 2018-12-07 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.

Hardware-Aware Probabilistic Machine Learning Models

Author : Laura Isabel Galindez Olascoaga
Publisher : Springer Nature
ISBN 13 : 3030740420
Total Pages : 163 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Hardware-Aware Probabilistic Machine Learning Models by : Laura Isabel Galindez Olascoaga

Download or read book Hardware-Aware Probabilistic Machine Learning Models written by Laura Isabel Galindez Olascoaga and published by Springer Nature. This book was released on 2021-05-19 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : Sandeep Saini
Publisher : CRC Press
ISBN 13 : 1000523845
Total Pages : 292 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis VLSI and Hardware Implementations using Modern Machine Learning Methods by : Sandeep Saini

Download or read book VLSI and Hardware Implementations using Modern Machine Learning Methods written by Sandeep Saini and published by CRC Press. This book was released on 2021-12-31 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Deep Learning at Scale

Author : Suneeta Mall
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098145240
Total Pages : 404 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Deep Learning at Scale by : Suneeta Mall

Download or read book Deep Learning at Scale written by Suneeta Mall and published by "O'Reilly Media, Inc.". This book was released on 2024-06-18 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bringing a deep-learning project into production at scale is quite challenging. To successfully scale your project, a foundational understanding of full stack deep learning, including the knowledge that lies at the intersection of hardware, software, data, and algorithms, is required. This book illustrates complex concepts of full stack deep learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project. A scaling effort is only beneficial when it's effective and efficient. To that end, this guide explains the intricate concepts and techniques that will help you scale effectively and efficiently. You'll gain a thorough understanding of: How data flows through the deep-learning network and the role the computation graphs play in building your model How accelerated computing speeds up your training and how best you can utilize the resources at your disposal How to train your model using distributed training paradigms, i.e., data, model, and pipeline parallelism How to leverage PyTorch ecosystems in conjunction with NVIDIA libraries and Triton to scale your model training Debugging, monitoring, and investigating the undesirable bottlenecks that slow down your model training How to expedite the training lifecycle and streamline your feedback loop to iterate model development A set of data tricks and techniques and how to apply them to scale your training model How to select the right tools and techniques for your deep-learning project Options for managing the compute infrastructure when running at scale