  Stochastic diffusion processes are pervasive in nature, from the seemingly
erratic Brownian motion to the complex interactions of synaptically-coupled
spiking neurons. Recently, drawing inspiration from Langevin dynamics,
neuromorphic diffusion models were proposed and have become one of the major
breakthroughs in the field of generative artificial intelligence. Unlike
discriminative models that have been well developed to tackle classification or
regression tasks, diffusion models as well as other generative models such as
ChatGPT aim at creating content based upon contexts learned. However, the more
complex algorithms of these models result in high computational costs using
today's technologies, creating a bottleneck in their efficiency, and impeding
further development. Here, we develop a spintronic voltage-controlled
magnetoelectric memory hardware for the neuromorphic diffusion process. The
in-memory computing capability of our spintronic devices goes beyond current
Von Neumann architecture, where memory and computing units are separated.
Together with the non-volatility of magnetic memory, we can achieve high-speed
and low-cost computing, which is desirable for the increasing scale of
generative models in the current era. We experimentally demonstrate that the
hardware-based true random diffusion process can be implemented for image
generation and achieve comparable image quality to software-based training as
measured by the Frechet inception distance (FID) score, achieving ~10^3 better
energy-per-bit-per-area over traditional hardware.
