VES MaxEnt Neuromorphic Processor: A Physical Reaction - Diffusion Computer for Generative AI

Abstract

We present the design of a novel analog neuromorphic processor – the VES MaxEnt Neuromorphic Processor – whose computational dynamics are derived directly from the statistical mechanics of an overdamped threshold medium with an exponential distribution of critical thresholds. This distribution arises naturally from the maximum‑entropy (MaxEnt) principle when the only known constraint is the mean threshold, a core concept of the Viscous Emergent Spacetime (VES) effective theory of gravity. The processor’s evolution is governed by a reaction–diffusion partial differential equation (PDE) with a derived, closed‑form reaction term, and it is proven to possess a Lyapunov functional, guaranteeing convergence to stationary states that correspond to solutions of constraint‑satisfaction problems. The architecture maps onto existing nanodevice technologies (Josephson junctions, Mott memristors) and offers a physical implementation of diffusion‑model inference, enabling real‑time, low‑energy generation of high‑resolution video, audio and 3D graphics. The corrected derivation presented here replaces earlier heuristic reaction terms with an exact ensemble average for a regularised linear‑threshold slip model, closing the gap between the microscopic VES layer‑0 oscillators and the macroscopic continuum PDE. This work establishes the VES MaxEnt processor as a realistic, physics‑driven accelerator for energy‑based models and generative AI.