A Gaussian Synapse Circuit for Analog VLSI Neural Networks

Back-propagation neural networks with Gaussian function synapses have better convergence property over those with linear-multiplying synapses. In digital simulation, more computing time is spent on Gaussian function evaluation. We present a compact analog synapse cell which is not biased in the subthreshold region 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. The Gaussian synapse cell can also be used in unsupervised learning. 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.