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Author: Mohammad Ghasemzadeh Publisher: ISBN: Category : Languages : en Pages : 98
Book Description
This thesis introduces novel frameworks for automated customization of two classes of machine learning algorithms, deep neural networks and causal Bayesian analysis. The high computational complexity often prohibits the deployment of ML models on resource-constrained embedded devices where memory and energy budgets are strictly limited. FPGAs offer a flexible substrate that can be configured to maximally exploit the parallel nature of computations in different ML algorithms to deliver high-throughput and power-efficient accelerators. To make FPGAs a ubiquitous platform for ML inference, automated frameworks that can customize ML models to the constraints of the underlying hardware and pertinent application requirements are necessary. My work proposes hardware-algorithm co-design approaches to customize ML inference on FPGA platforms and provides end-to-end automated frameworks to generate optimized hardware accelerators which can be used by a broad range of ML developers without requiring any hardware design knowledge. My key contributions include: (i) proposing an end-to-end framework to customize execution of deep neural networks on FPGAs using a reconfigurable encoding approach for the parameters of model which results in 9-fold reduction in memory footprint and 15-fold improvement in throughput without any loss in accuracy, (ii) proposing CausaLearn, the first automated framework that enables real-time and scalable approximation of probability density function in the context of causal Bayesian analysis which offers up to two orders-of-magnitude runtime and energy improvements compared to the best-known prior solution, (iii) proposing ReBNet, an end-to-end framework for training reconfigurable binary neural networks on software and developing efficient accelerators for execution on FPGA.
Author: Mohammad Ghasemzadeh Publisher: ISBN: Category : Languages : en Pages : 98
Book Description
This thesis introduces novel frameworks for automated customization of two classes of machine learning algorithms, deep neural networks and causal Bayesian analysis. The high computational complexity often prohibits the deployment of ML models on resource-constrained embedded devices where memory and energy budgets are strictly limited. FPGAs offer a flexible substrate that can be configured to maximally exploit the parallel nature of computations in different ML algorithms to deliver high-throughput and power-efficient accelerators. To make FPGAs a ubiquitous platform for ML inference, automated frameworks that can customize ML models to the constraints of the underlying hardware and pertinent application requirements are necessary. My work proposes hardware-algorithm co-design approaches to customize ML inference on FPGA platforms and provides end-to-end automated frameworks to generate optimized hardware accelerators which can be used by a broad range of ML developers without requiring any hardware design knowledge. My key contributions include: (i) proposing an end-to-end framework to customize execution of deep neural networks on FPGAs using a reconfigurable encoding approach for the parameters of model which results in 9-fold reduction in memory footprint and 15-fold improvement in throughput without any loss in accuracy, (ii) proposing CausaLearn, the first automated framework that enables real-time and scalable approximation of probability density function in the context of causal Bayesian analysis which offers up to two orders-of-magnitude runtime and energy improvements compared to the best-known prior solution, (iii) proposing ReBNet, an end-to-end framework for training reconfigurable binary neural networks on software and developing efficient accelerators for execution on FPGA.
Author: Publisher: Academic Press ISBN: 0128231246 Category : Mathematics Languages : en Pages : 416
Book Description
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
Author: Sandeep Saini Publisher: CRC Press ISBN: 1000523810 Category : Technology & Engineering Languages : en Pages : 329
Book Description
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.
Author: Ibrahim (Abe) M. Elfadel Publisher: Springer ISBN: 3030046664 Category : Technology & Engineering Languages : en Pages : 697
Book Description
This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other....As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center
Author: Donald G. Bailey Publisher: John Wiley & Sons ISBN: 1119819792 Category : Technology & Engineering Languages : en Pages : 501
Book Description
Design for Embedded Image Processing on FPGAs Bridge the gap between software and hardware with this foundational design reference Field-programmable gate arrays (FPGAs) are integrated circuits designed so that configuration can take place. Circuits of this kind play an integral role in processing images, with FPGAs increasingly embedded in digital cameras and other devices that produce visual data outputs for subsequent realization and compression. These uses of FPGAs require specific design processes designed to mediate smoothly between hardware and processing algorithm. Design for Embedded Image Processing on FPGAs provides a comprehensive overview of these processes and their applications in embedded image processing. Beginning with an overview of image processing and its core principles, this book discusses specific design and computation techniques, with a smooth progression from the foundations of the field to its advanced principles. Readers of the second edition of Design for Embedded Image Processing on FPGAs will also find: Detailed discussion of image processing techniques including point operations, histogram operations, linear transformations, and more New chapters covering Deep Learning algorithms and Image and Video Coding Example applications throughout to ground principles and demonstrate techniques Design for Embedded Image Processing on FPGAs is ideal for engineers and academics working in the field of Image Processing, as well as graduate students studying Embedded Systems Engineering, Image Processing, Digital Design, and related fields.
Author: Mohamed Ben Ahmed Publisher: Springer Nature ISBN: 3030941914 Category : Technology & Engineering Languages : en Pages : 1117
Book Description
This book sets the innovative research contributions, works, and solutions for almost all the intelligent and smart applications in the smart cities. The smart city concept is a relevant topic for industrials, governments, and citizens. Due to this, the smart city, considered as a multi-domain context, attracts tremendously academics researchers and practitioners who provide efforts in theoretical proofs, approaches, architectures, and in applied researches. The importance of smart cities comes essentially from the significant growth of populations in the near future which conducts to a real need of smart applications that can support this evolution in the future cities. The main scope of this book covers new and original ideas for the next generations of cities using the new technologies. The book involves the application of the data science and AI, IoT technologies and architectures, smart earth and water management, smart education and E-learning systems, smart modeling systems, smart mobility, and renewable energy. It also reports recent research works on big data technologies, image processing and recognition systems, and smart security and privacy.
Author: Chao Wang Publisher: CRC Press ISBN: 1040031986 Category : Computers Languages : en Pages : 417
Book Description
With the end of Moore’s Law, domain-specific architecture (DSA) has become a crucial mode of implementing future computing architectures. This book discusses the system-level design methodology of DSAs and their applications, providing a unified design process that guarantees functionality, performance, energy efficiency, and real-time responsiveness for the target application. DSAs often start from domain-specific algorithms or applications, analyzing the characteristics of algorithmic applications, such as computation, memory access, and communication, and proposing the heterogeneous accelerator architecture suitable for that particular application. This book places particular focus on accelerator hardware platforms and distributed systems for various novel applications, such as machine learning, data mining, neural networks, and graph algorithms, and also covers RISC-V open-source instruction sets. It briefly describes the system design methodology based on DSAs and presents the latest research results in academia around domain-specific acceleration architectures. Providing cutting-edge discussion of big data and artificial intelligence scenarios in contemporary industry and typical DSA applications, this book appeals to industry professionals as well as academicians researching the future of computing in these areas.
Author: Christoforos Kachris Publisher: Springer ISBN: 3319927922 Category : Technology & Engineering Languages : en Pages : 280
Book Description
This book provides readers with an overview of the architectures, programming frameworks, and hardware accelerators for typical cloud computing applications in data centers. The authors present the most recent and promising solutions, using hardware accelerators to provide high throughput, reduced latency and higher energy efficiency compared to current servers based on commodity processors. Readers will benefit from state-of-the-art information regarding application requirements in contemporary data centers, computational complexity of typical tasks in cloud computing, and a programming framework for the efficient utilization of the hardware accelerators.
Author: Mohammad Ghasemzadeh
Author: Mohammad Ghasemzadeh
Author: 
Author: Sandeep Saini
Author: Ibrahim (Abe) M. Elfadel
Author: Donald G. Bailey
Author: Juan Antonio Lossio-Ventura
Author: Mohamed Ben Ahmed
Author: Kamarul Arifin Ahmad
Author: Chao Wang
Author: Christoforos Kachris