Convolution Inference via Synchronization of a Coupled CMOS Oscillator Array

Abstract

Oscillator neural networks (ONNs) are a promising hardware option for artificial intelligence. With an abundance of theoretical treatments of ONNs, few experimental implementations exist to date. In contrast to prior publications of only building block functionality, we report a practical experimental demonstration of neural computing using an ONN. The arrays contain 26 CMOS ring oscillators in the GHz range of frequencies are tuned by image data and convolution kernels. Synchronization of oscillators results in an analog output voltage approximating convolution neural network operation.