A compact low-power CMOS analog FSR model-based CNN

A compact low-power CMOS analog circuit implementation of a Cellular Neural Network based on Full Signal Range Model (FSR-CNN) is presented. The required operations in cell definition are synapses (multiplication and summation) and saturated integration. In each synapse a new multiplier architecture is employed with voltage and current inputs and current output, which allows sharing building blocks and using continuously programmable weight values. Feasibility and usefulness of the proposed FSR cell architecture is verified through the connected component detector application.