(Invited) High Performance Stochastic Memristive Networks for Neurocomputing and Neurooptimization

Abstract

Stochastic neural networks have become the state-of-the-art approach for solving problems in machine learning, information theory, and statistics. The key operation in such networks is a stochastic dot-product. While there have been many demonstrations of dot-product circuits and, separately, of stochastic neurons, the efficient hardware implementation combining both functionalities is still missing. In my talk I will discuss our recent work on addressing this need. I will first discuss very compact, fast, energy-efficient, and scalable implementation of stochastic dot-product circuits based on passively integrated metal-oxide memristors. The high performance of such circuits is due to mixed-signal implementation, while the efficient stochastic operation is achieved by utilizing circuit’s noise, intrinsic and/or extrinsic to the memory cell array. The dynamic scaling of weights, enabled by analog memory devices, allows for efficient realization of different annealing approaches to improve functionality. I will then discuss several application demonstrations based on passively integrated TiO2-x memristors, including Hopfield networks for solving combinatorial optimization problems, and a Boltzmann machine.