DocumentCode
3547331
Title
An adaptive analog synapse circuit that implements the least-mean-square learning rule
Author
Srinivasan, Venkatesh ; Dugger, Jeff ; Hasler, Paul
Author_Institution
Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
fYear
2005
fDate
23-26 May 2005
Firstpage
4441
Abstract
In this paper a compact adaptive analog synapse circuit that implements the least-mean-square (LMS) learning rule is described. Basic simulation results demonstrate the LMS learning rule in the proposed circuit. An adaptive linear combiner that uses the proposed synapse is shown to learn a square wave that matches closely with the desired target. Issues of weight decay and its implications to the design of the synapse circuit are presented as well. The synapse is designed in a 0.5 μm CMOS technology.
Keywords
CMOS integrated circuits; adaptive filters; learning (artificial intelligence); least mean squares methods; neural nets; 0.5 micron; CMOS technology; LMS learning rule; adaptive analog synapse circuit; adaptive linear combiner; least-mean-square learning rule; weight decay; Adaptation model; Adaptive filters; Adaptive systems; CMOS technology; Circuit simulation; Computational modeling; Least squares approximation; Nonvolatile memory; Transversal filters; Tunneling;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 2005. ISCAS 2005. IEEE International Symposium on
Print_ISBN
0-7803-8834-8
Type
conf
DOI
10.1109/ISCAS.2005.1465617
Filename
1465617
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