Gaussian synapse circuit for analog VLSI neural networks

Joongho Choi, Bing J. Sheu, Josephine C.F. Chang

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


Back-propagation neural networks with Gaussian function synapses have better convergence property over those with linear-multiplying synapses. A compact analog Gaussian synapse cell which is not biased in the subthreshold region has been designed for fully-parallel operation. This cell can approximate a Gaussian function with accuracy around 98% in the ideal case. Device mismatch induced by fabrication process will cause some degradation to this approximation. Programmability of the proposed Gaussian synapse cell is achieved by changing the stored synapse weight Wji, the reference current and the sizes of transistors in the differential pair.

Original languageEnglish
Pages (from-to)483-486
Number of pages4
JournalProceedings - IEEE International Symposium on Circuits and Systems
StatePublished - 1994
EventProceedings of the 1994 IEEE International Symposium on Circuits and Systems. Part 3 (of 6) - London, England
Duration: 30 May 19942 Jun 1994


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