Arslan Munir's Books

Modeling and Optimization of Parallel and Distributed Embedded Systems

By: Arslan Munir,Ann Gordon-Ross,Sanjay Ranka

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Modeling and Optimization of Parallel and Distributed Embedded Systems

By: Arslan Munir,Ann Gordon-Ross,Sanjay Ranka

This book introduces the state-of-the-art in research in parallel and distributed embedded systems, which have been enabled by developments in silicon technology, micro-electro-mechanical systems (MEMS), wireless communications, computer networking, and digital electronics. These systems have diverse applications in domains including military and defense, medical, automotive, and unmanned autonomous vehicles. The emphasis of the book is on the modeling and optimization of emerging parallel and distributed embedded systems in relation to the three key design metrics of performance, power and dependability. Key features: Includes an embedded wireless sensor networks case study to help illustrate the modeling and optimization of distributed embedded systems. Provides an analysis of multi-core/many-core based embedded systems to explain the modeling and optimization of parallel embedded systems. Features an application metrics estimation model; Markov modeling for fault tolerance and analysis; and queueing theoretic modeling for performance evaluation. Discusses optimization approaches for distributed wireless sensor networks; high-performance and energy-efficient techniques at the architecture, middleware and software levels for parallel multicore-based embedded systems; and dynamic optimization methodologies. Highlights research challenges and future research directions. The book is primarily aimed at researchers in embedded systems; however, it will also serve as an invaluable reference to senior undergraduate and graduate students with an interest in embedded systems research.

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Page Count

399

Publisher

John Wiley & Sons

Published Date

ISBN 10

1119086418

ISBN 13

9781119086413

Accelerators for Convolutional Neural Networks

By: Arslan Munir,Joonho Kong,Mahmood Azhar Qureshi

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Accelerators for Convolutional Neural Networks

By: Arslan Munir,Joonho Kong,Mahmood Azhar Qureshi

Accelerators for Convolutional Neural Networks Comprehensive and thorough resource exploring different types of convolutional neural networks and complementary accelerators Accelerators for Convolutional Neural Networks provides basic deep learning knowledge and instructive content to build up convolutional neural network (CNN) accelerators for the Internet of things (IoT) and edge computing practitioners, elucidating compressive coding for CNNs, presenting a two-step lossless input feature maps compression method, discussing arithmetic coding -based lossless weights compression method and the design of an associated decoding method, describing contemporary sparse CNNs that consider sparsity in both weights and activation maps, and discussing hardware/software co-design and co-scheduling techniques that can lead to better optimization and utilization of the available hardware resources for CNN acceleration. The first part of the book provides an overview of CNNs along with the composition and parameters of different contemporary CNN models. Later chapters focus on compressive coding for CNNs and the design of dense CNN accelerators. The book also provides directions for future research and development for CNN accelerators. Other sample topics covered in Accelerators for Convolutional Neural Networks include: How to apply arithmetic coding and decoding with range scaling for lossless weight compression for 5-bit CNN weights to deploy CNNs in extremely resource-constrained systems State-of-the-art research surrounding dense CNN accelerators, which are mostly based on systolic arrays or parallel multiply-accumulate (MAC) arrays iMAC dense CNN accelerator, which combines image-to-column (im2col) and general matrix multiplication (GEMM) hardware acceleration Multi-threaded, low-cost, log-based processing element (PE) core, instances of which are stacked in a spatial grid to engender NeuroMAX dense accelerator Sparse-PE, a multi-threaded and flexible CNN PE core that exploits sparsity in both weights and activation maps, instances of which can be stacked in a spatial grid for engendering sparse CNN accelerators For researchers in AI, computer vision, computer architecture, and embedded systems, along with graduate and senior undergraduate students in related programs of study, Accelerators for Convolutional Neural Networks is an essential resource to understanding the many facets of the subject and relevant applications.

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Page Count

308

Publisher

John Wiley & Sons

Published Date

ISBN 10

1394171900

ISBN 13

9781394171903

September 11 Fiction of Matrix

By: Faruk Arslan

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September 11 Fiction of Matrix

By: Faruk Arslan

I was inspired by Hollywood movie in giving a name to this book. Matrix is the name of a fake world which is believed to be fictitious. September 11 was a starting point which differentiates between those who resist and those who don't and between those who believe and those who don't. I agree with war on terrorism, blame all kind of terrorist and terror acts, but I disagree with the fiction of Matrix. Absolutely, Al Qaeda is a terrorist organization and Laden is the mastermind of terrorism. Thank you Canada. I am proud of being Canadian, otherwise I would not feel free to write this book in US. FARUK ARSLAN REGINA, SASKATCHEWAN, CANADA

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