Probabilistic Circuits for Autonomous Learning: A simulation study

TL;DR

This paper introduces a novel autonomous probabilistic circuit that enables fast, energy-efficient learning without digital computation, demonstrated through SPICE simulations for potential mobile and edge computing applications.

Contribution

It presents a fully autonomous, analog probabilistic circuit for learning, avoiding digital computation, and showcases its feasibility via SPICE simulation results.

Findings

Demonstrated a clockless autonomous circuit for learning.

Readout of synaptic weights as analog voltages.

Potential for mobile and edge computing applications.

Abstract

Modern machine learning is based on powerful algorithms running on digital computing platforms and there is great interest in accelerating the learning process and making it more energy efficient. In this paper we present a fully autonomous probabilistic circuit for fast and efficient learning that makes no use of digital computing. Specifically we use SPICE simulations to demonstrate a clockless autonomous circuit where the required synaptic weights are read out in the form of analog voltages. Such autonomous circuits could be particularly of interest as standalone learning devices in the context of mobile and edge computing.