h1

h2

h3

h4

h5
h6
http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png

Scalable Time-Domain Compute-in-Memory BNN Engine with 2.06 POPS/W Energy Efficiency for Edge-AI Devices

; ; ;

In
Proceedings of the Great Lakes Symposium on VLSI 2023, Seiten/Artikel-Nr: 665-670

Konferenz/Event:Great Lakes Symposium on VLSI 2023 , Knoxville TN USA , USA , GLSVLSI '23 , 2023-06-05 - 2023-06-07

ImpressumNew York, NY, USA : ACM

Umfang665-670

ISBN979-8-40-070125-2, 979-8-4007-0125-2

Online
DOI: 10.1145/3583781.3590220

URL: https://publications.rwth-aachen.de/record/959691/files/959691.pdf

Einrichtungen

  1. Lehrstuhl für Integrierte digitale Systeme und Schaltungsentwurf (611110)


Restricted:
Download fulltext PDF

Dokumenttyp
Contribution to a book/Contribution to a conference proceedings

Format
online

Sprache
English

Anmerkung
Peer reviewed article

Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-85163195429
WOS Core Collection: WOS:001042307500119

Interne Identnummern
RWTH-2023-05784
Datensatz-ID: 959691

Beteiligte Länder
Germany

 GO

QR Code for this record

The record appears in these collections:
Document types > Events > Contributions to a conference proceedings
Document types > Books > Contributions to a book
Faculty of Electrical Engineering and Information Technology (Fac.6)
Public records
Publications database
611110
 Record created 2023-06-13, last modified 2025-02-07
Similar records


Restricted:
Download fulltext PDF
Rate this document:

Rate this document:
1
2
3
4
5
 
(Not yet reviewed)