NDLI: The rebirth of neural networks

Content Provider	ACM Digital Library
Author	Temam, Olivier
Abstract	After the hype of the 1990s, where companies like Intel or Philips built commercial hardware systems based on neural networks, the approach quickly lost ground for multiple reasons: hardware neural networks were no match for software neural networks run on rapidly progressing general-purpose processors, their application scope was considered too limited, and even progress in machine-learning theory overshadowed neural networks. However, in the past few years, a remarkable convergence of trends and innovations is casting a new light on neural networks and could make them valuable components of future computing systems. Trends in technology call for architectures which can sustain a large number of defects, something neural networks are intrinsically capable of. Tends in applications, summarized in the recent RMS categorization, highlight a number of key algorithms which are eligible to neural networks implementations. At the same time, innovations in technology, such as the recent realization of a memristor, are creating the conditions for the efficient hardware implementation of neural networks. Innovations in machine learning, with the recent advent of Deep Networks, have revived interest in neural networks. Finally, recent findings in neurobiology carry even greater prospects, where detailed explanations of how complex functions, such as vision, can be implemented further open up the defect-tolerance and application potential of neural network architectures.
Starting Page	349
Ending Page	349
Page Count	1
File Format	PDF
ISSN	01635964
DOI	10.1145/1816038.1816008
Journal	ACM SIGARCH Computer Architecture News (CARN)
Volume Number	38
Issue Number	3
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1981-04-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Neural networks
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

The rebirth of neural networks

The rebirth of APL?

Rebirth of the computer industry

Classification by ensembles of neural networks

Neural networks in J

Neural networks in APL

Time in neural networks

Software for neural networks

Organization of Neural Networks in the Neocortex

The rebirth of neural networks

Similar Documents

The rebirth of neural networks

The rebirth of APL?

Rebirth of the computer industry

Classification by ensembles of neural networks

Neural networks in J

Neural networks in APL

Time in neural networks

Software for neural networks

Organization of Neural Networks in the Neocortex

The rebirth of neural networks