Optimizing an Analog Neuron Circuit Design for Nonlinear Function Approximation

Abstract

Silicon neurons designed using subthreshold analog-circuit techniques offer low power and compact area but are exponentially sensitive to threshold-voltage mismatch in transistors. The resulting heterogeneity in the neurons' responses, however, provides a diverse set of basis functions for smooth nonlinear function approximation. For low-order polynomials, neuron spiking thresholds ought to be distributed uniformly across the function's domain. This uniform distribution is difficult to achieve solely by sizing transistors to titrate mismatch. With too much mismatch, many neuron's thresholds fall outside the domain (i.e. they either always spike or remain silent). With too little mismatch, all their thresholds bunch up in the middle of the domain. Here, we present a silicon-neuron design methodology that minimizes overall area by optimizing transistor sizes in concert with a few locally-stored programmable bits to adjust each neuron's offset (and gain). We validated this methodology in a 28-nm mixed analog-digital CMOS process. Compared to relying on mismatch alone, augmentation with digital correction effectively reduced silicon area by 38%.