ReRAM based Machine Learning

Serving as a bridge between researchers in the computing domain and computing hardware designers, this book presents ReRAM techniques for distributed computing using IMC accelerators, ReRAM-based IMC architectures for machine learning (ML) ...

ReRAM based Machine Learning

Serving as a bridge between researchers in the computing domain and computing hardware designers, this book presents ReRAM techniques for distributed computing using IMC accelerators, ReRAM-based IMC architectures for machine learning (ML) and data-intensive applications, and strategies to map ML designs onto hardware accelerators.

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ReRAM-based Machine Learning
Language: en
Pages: 259
Authors: Hao Yu, Leibin Ni, Sai Manoj Pudukotai Dinakarrao
Categories: Computers
Type: BOOK - Published: 2021-03-05 - Publisher: IET

Serving as a bridge between researchers in the computing domain and computing hardware designers, this book presents ReRAM techniques for distributed computing using IMC accelerators, ReRAM-based IMC architectures for machine learning (ML) and data-intensive applications, and strategies to map ML designs onto hardware accelerators.
Machine Learning in VLSI Computer-Aided Design
Language: en
Pages: 694
Authors: Ibrahim (Abe) M. Elfadel, Duane S. Boning, Xin Li
Categories: Technology & Engineering
Type: BOOK - Published: 2019-03-15 - Publisher: Springer

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
Efficient Processing of Deep Neural Networks
Language: en
Pages: 341
Authors: Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, Joel S. Emer
Categories: Computers
Type: BOOK - Published: 2020-06-24 - Publisher: Morgan & Claypool Publishers

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
Sensing of Non-Volatile Memory Demystified
Language: en
Pages: 107
Authors: Swaroop Ghosh
Categories: Technology & Engineering
Type: BOOK - Published: 2018-08-10 - Publisher: Springer

This book introduces readers to the latest advances in sensing technology for a broad range of non-volatile memories (NVMs). Challenges across the memory technologies are highlighted and their solutions in mature technology are discussed, enabling innovation of sensing technologies for future NVMs. Coverage includes sensing techniques ranging from well-established NVMs
Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design
Language: en
Pages: 296
Authors: Nan Zheng, Pinaki Mazumder
Categories: Computers
Type: BOOK - Published: 2019-12-09 - Publisher: John Wiley & Sons

Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning applications This book focuses on how to build energy-efficient hardware for neural networks with learning capabilities—and provides co-design and co-optimization methodologies for building hardware neural networks that can learn. Presenting a complete picture from