Machine Learning Compilation Flow for a ReRAM-based Accelerator

Machine Learning Compilation Flow for a ReRAM-based Accelerator
Author :
Publisher :
Total Pages : 0
Release :
ISBN-10 : OCLC:1399546897
ISBN-13 :
Rating : 4/5 ( Downloads)

Book Synopsis Machine Learning Compilation Flow for a ReRAM-based Accelerator by : 廖敏君

Download or read book Machine Learning Compilation Flow for a ReRAM-based Accelerator written by 廖敏君 and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:


Machine Learning Compilation Flow for a ReRAM-based Accelerator Related Books

Machine Learning Compilation Flow for a ReRAM-based Accelerator
Language: en
Pages: 0
Authors: 廖敏君
Categories:
Type: BOOK - Published: 2022 - Publisher:

DOWNLOAD EBOOK

ReRAM-based Machine Learning
Language: en
Pages: 260
Authors: Hao Yu
Categories: Computers
Type: BOOK - Published: 2021-03-05 - Publisher: IET

DOWNLOAD EBOOK

Serving as a bridge between researchers in the computing domain and computing hardware designers, this book presents ReRAM techniques for distributed computing
Efficient Processing of Deep Neural Networks
Language: en
Pages: 254
Authors: Vivienne Sze
Categories: Technology & Engineering
Type: BOOK - Published: 2022-05-31 - Publisher: Springer Nature

DOWNLOAD EBOOK

This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are curren
Deep Learning for Computer Architects
Language: en
Pages: 109
Authors: Brandon Reagen
Categories: Technology & Engineering
Type: BOOK - Published: 2022-05-31 - Publisher: Springer Nature

DOWNLOAD EBOOK

Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solv
Gaussian Processes for Machine Learning
Language: en
Pages: 266
Authors: Carl Edward Rasmussen
Categories: Computers
Type: BOOK - Published: 2005-11-23 - Publisher: MIT Press

DOWNLOAD EBOOK

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machi