Reram-Based Machine Learning

Name: Reram-Based Machine Learning
Brand: INSTITUTION OF ENGINEERING & T
SKU: 9781839530814
Price: 48338 HUF
Availability: InStock

Szerző: Leibin Ni, Sai Manoj Pudukotai Dinakarrao

Nyelv:

Kötés: Kemény kötésű

Kiadó: INSTITUTION OF ENGINEERING & T

Elérhetőség: Beszállítói készleten

Küldés 9-15 napon belül

48 338 Ft

The transition towards exascale computing has resulted in major transformations in computing paradig...

Információk a könyvről

Szerző

Leibin Ni, Sai Manoj Pudukotai Dinakarrao

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2021

oldal

261

EAN

9781839530814

ISBN

1839530812

Enbook ID

33431718

Kiadó

INSTITUTION OF ENGINEERING & T

Súly

540

Méretek

160 x 239 x 18

Teljes leírás

The transition towards exascale computing has resulted in major transformations in computing paradigms. The need to analyze and respond to such large amounts of data sets has led to the adoption of machine learning (ML) and deep learning (DL) methods in a wide range of applications.

One of the major challenges is the fetching of data from computing memory and writing it back without experiencing a memory-wall bottleneck. To address such concerns, in-memory computing (IMC) and supporting frameworks have been introduced. In-memory computing methods have ultra-low power and high-density embedded storage. Resistive Random-Access Memory (ReRAM) technology seems the most promising IMC solution due to its minimized leakage power, reduced power consumption and smaller hardware footprint, as well as its compatibility with CMOS technology, which is widely used in industry.

In this book, the authors introduce ReRAM techniques for performing distributed computing using IMC accelerators, present ReRAM-based IMC architectures that can perform computations of ML and data-intensive applications, as well as strategies to map ML designs onto hardware accelerators.

The book serves as a bridge between researchers in the computing domain (algorithm designers for ML and DL) and computing hardware designers.

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Reram-Based Machine Learning

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