Thermodynamic-inspired computing with oscillatory neural networks

Introduction

There is a pressing need to address the power consumption of computing, which keeps rising to the point it has become an environmental concern. Despite the remarkable progress in semiconductor technology, computing architectures are still energy inefficient, engineered for deterministic tasks as well as susceptible to noise, heat, and variations.

Research Objective

Instead of massively over-designing architectures to compute with an acceptable degree of reliability, this research aims to “let physics do the computing” and harness noise, heat, and variabilities for energy-efficient computing.

Proposed Paradigm

At the heart of the proposed paradigm is the thermodynamics of open systems entwined with neuromorphic computing. THERMODON aims to develop an unconventional neuromorphic architecture to thermodynamically compute and self-organize (“learn”).

Hypothesis

I hypothesize that the natural thermodynamics of appropriately engineered architecture can harness noise, heat, and variations to self-organize toward energy-efficient “solutions” to “problems” posed by external potentials.

Methodology

I will develop such architecture with neuromorphic oscillatory neural networks that I master in my lab. This research aims to address how thermodynamic principles can be applied to oscillatory neural networks to derive learning rules that are:

1. Unsupervised
2. Continuously adapting
3. Transforming the architecture into a dynamic “self-organizing” and “open interactive” system that learns, infers, and interacts with the environment.

Expected Outcomes

THERMODON will bring breakthrough innovations in thermodynamic computing models and AI-specialized hardware to enable online training and inference to intelligent systems.

Interdisciplinary Research

The interdisciplinary research in this project between neuromorphic computing and thermodynamics opens a new and exciting area in computer architecture, triggering a paradigm shift in edge AI computing as well as an immediate impact as a hardware accelerator platform.