Self-training superconducting neuromorphic circuits using reinforcement learning rules

TL;DR

This paper presents superconducting neuromorphic circuits that use reinforcement learning rules for self-training, enabling rapid, local weight updates without external programming or back-propagation, demonstrated through SPICE simulations.

Contribution

It introduces a novel superconducting hardware implementation of reinforcement learning-based local weight updates for neuromorphic circuits, eliminating the need for explicit weight programming.

Findings

Neural network training time is approximately one nanosecond.

Weights are updated based on local information and a global reinforcement signal.

The approach simplifies hardware design by removing the need for back-propagation circuitry.

Abstract

Reinforcement learning algorithms are used in a wide range of applications, from gaming and robotics to autonomous vehicles. In this paper we describe a set of reinforcement learning-based local weight update rules and their implementation in superconducting hardware. Using SPICE circuit simulations, we implement a small-scale neural network with a learning time of order one nanosecond. This network can be trained to learn new functions simply by changing the target output for a given set of inputs, without the need for any external adjustments to the network. In this implementation the weights are adjusted based on the current state of the overall network response and locally stored information about the previous action. This removes the need to program explicit weight values in these networks, which is one of the primary challenges that analog hardware implementations of neural networks face. The adjustment of weights is based on a global reinforcement signal that obviates the need for circuitry to back-propagate errors.