Thermodynamic AI and Thermodynamic Linear Algebra

Patrick J. Coles, Maxwell Aifer, Kaelan Donatella, Denis Melanson, Max Hunter Gordon, Thomas Dybdahl Ahle, Daniel Simpson, Gavin Crooks, Antonio J Martinez, Faris Mouti Sbahi

Keywords: AI, artificial intelligence, thermodynamics, computing, linear algebra, analog, circuits, diffusion models

TL;DR: We propose a novel computing paradigm, called Thermodynamic AI, that exploits physical stochasticity to accelerate generative AI, probabilistic AI, and linear algebra.

Abstract: Many Artificial Intelligence (AI) algorithms are inspired by physics and employ stochastic fluctuations, such as generative diffusion models, Bayesian neural networks, and Monte Carlo inference. These algorithms are currently run on digital hardware, ultimately limiting their scalability and overall potential. Here, we propose a novel computing device, called Thermodynamic AI hardware, that could accelerate such algorithms. Thermodynamic AI hardware can be viewed as a novel form of computing, since it uses novel fundamental building blocks, called stochastic units (s-units), which naturally evolve over time via stochastic trajectories. In addition to these s-units, Thermodynamic AI hardware employs a Maxwell's demon device that guides the system to produce non-trivial states. We provide a few simple physical architectures for building these devices, such as RC electrical circuits. Moreover, we show that this same hardware can be used to accelerate various linear algebra primitives. We present simple thermodynamic algorithms for (1) solving linear systems of equations, (2) computing matrix inverses, (3) computing matrix determinants, and (4) solving Lyapunov equations. Under reasonable assumptions, we rigorously establish asymptotic speedups for our algorithms, relative to digital methods, that scale linearly in dimension. Numerical simulations also suggest a speedup is achievable in practical scenarios.

Submission Number: 21