In-Memory Computing Architectures for Big Data and Machine Learning Applications

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10252016" target="_blank" >RIV/61989100:27240/22:10252016 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-981-19-8069-5_2" target="_blank" >https://link.springer.com/chapter/10.1007/978-981-19-8069-5_2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-19-8069-5_2" target="_blank" >10.1007/978-981-19-8069-5_2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

In-Memory Computing Architectures for Big Data and Machine Learning Applications

Popis výsledku v původním jazyce

Traditional computing hardware is working to meet the extensive computational load presented by the rapidly growing Machine Learning (ML) and Artificial Intelligence algorithms such as Deep Neural Networks and Big Data. In order to get hardware solutions to meet the low-latency and high-throughput computational needs of these algorithms, Non-Von Neumann computing architectures such as In-memory Computing (IMC) have been extensively researched and experimented with over the last five years. This study analyses and reviews works designed to accelerate Machine Learning task. We investigate different architectural aspects and directions and provide our comparative evaluations. We further discuss IMC research&apos;s challenges and limitations and present possible directions.

Název v anglickém jazyce

In-Memory Computing Architectures for Big Data and Machine Learning Applications

Popis výsledku anglicky

Traditional computing hardware is working to meet the extensive computational load presented by the rapidly growing Machine Learning (ML) and Artificial Intelligence algorithms such as Deep Neural Networks and Big Data. In order to get hardware solutions to meet the low-latency and high-throughput computational needs of these algorithms, Non-Von Neumann computing architectures such as In-memory Computing (IMC) have been extensively researched and experimented with over the last five years. This study analyses and reviews works designed to accelerate Machine Learning task. We investigate different architectural aspects and directions and provide our comparative evaluations. We further discuss IMC research&apos;s challenges and limitations and present possible directions.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

FUTURE DATA AND SECURITY ENGINEERING. BIG DATA, SECURITY AND PRIVACY, SMART CITY AND INDUSTRY 4.0 APPLICATIONS, FDSE 2022

ISBN

978-981-19806-9-5

ISSN

1865-0929

e-ISSN

1865-0937

Počet stran výsledku

15

Strana od-do

19-33

Název nakladatele

SPRINGER INTERNATIONAL PUBLISHING AG

Místo vydání

CHAM

Místo konání akce

Ho Chi Minh

Datum konání akce

23. 11. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000921145600002