Stochastic Neuromorphic Circuits for Solving MAXCUT

TL;DR

This paper introduces neuromorphic circuits that leverage intrinsic device randomness to efficiently solve the MAXCUT problem, demonstrating advantages over traditional software methods and highlighting the potential of stochastic neuromorphic architectures.

Contribution

The work presents a novel neuromorphic hardware approach that uses intrinsic stochasticity of devices to solve MAXCUT, combining principles of neuromorphic computing with stochastic optimization.

Findings

Neuromorphic circuits effectively solve MAXCUT using device randomness.

Hardware performs favorably compared to software solvers.

Proposes scalable neuromorphic architecture leveraging stochasticity.

Abstract

Finding the maximum cut of a graph (MAXCUT) is a classic optimization problem that has motivated parallel algorithm development. While approximate algorithms to MAXCUT offer attractive theoretical guarantees and demonstrate compelling empirical performance, such approximation approaches can shift the dominant computational cost to the stochastic sampling operations. Neuromorphic computing, which uses the organizing principles of the nervous system to inspire new parallel computing architectures, offers a possible solution. One ubiquitous feature of natural brains is stochasticity: the individual elements of biological neural networks possess an intrinsic randomness that serves as a resource enabling their unique computational capacities. By designing circuits and algorithms that make use of randomness similarly to natural brains, we hypothesize that the intrinsic randomness in microelectronics devices could be turned into a valuable component of a neuromorphic architecture enabling more efficient computations. Here, we present neuromorphic circuits that transform the stochastic behavior of a pool of random devices into useful correlations that drive stochastic solutions to MAXCUT. We show that these circuits perform favorably in comparison to software solvers and argue that this neuromorphic hardware implementation provides a path for scaling advantages. This work demonstrates the utility of combining neuromorphic principles with intrinsic randomness as a computational resource for new computational architectures.