DocumentCode :
2074211
Title :
Neuromorphic CMOL circuits
Author :
Likharev, Konstantin K.
Author_Institution :
Stony Brook Univ., NY, USA
Volume :
1
fYear :
2003
fDate :
12-14 Aug. 2003
Firstpage :
339
Abstract :
This is a brief review of the recent work on the development of neuromorphic architectures for future hybrid CMOS/nanowire/MOLecular ("CMOL") circuits. Such circuits may provide the first chance for the implementation of advanced information processing systems with areal density of (beyond 1012 active functions per cm2) comparable to that of the human cerebral cortex, while operating at much higher speed (up to 1020 operations per second per cm2), at acceptable power consumption. Our group has suggested a family of distributed crosspoint networks ("CrossNets") that are natural for implementation in CMOL technology, and has shown that such networks may be trained to perform at least the effective pattern recognition in the Hopfield mode. Work on CrossNet training to perform more complex tasks in under way.
Keywords :
CMOS analogue integrated circuits; nanowires; neural net architecture; pattern recognition; power consumption; reviews; Hopfield mode; areal density; crossnet training; crossnets; crosspoint networks distribution; human cerebral cortex; hybrid CMOS circuits; information processing systems; molecular circuits; nanowire circuits; neuromorphic architectures; neuromorphic circuits; power consumption; CMOS logic circuits; CMOS technology; Fabrication; MOSFETs; Nanoscale devices; Neuromorphics; Self-assembly; Single electron devices; Single electron transistors; Very large scale integration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Nanotechnology, 2003. IEEE-NANO 2003. 2003 Third IEEE Conference on
Print_ISBN :
0-7803-7976-4
Type :
conf
DOI :
10.1109/NANO.2003.1231787
Filename :
1231787
Link To Document :
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