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
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;
Conference_Titel :
Circuits and Systems, 2005. ISCAS 2005. IEEE International Symposium on
Print_ISBN :
0-7803-8834-8
DOI :
10.1109/ISCAS.2005.1465617
