An efficient probabilistic hardware architecture for diffusion-like models
Abstract
Abstract The proliferation of probabilistic AI has prompted proposals for specialized stochastic computers. Despite promising efficiency gains, these proposals have failed to gain traction because they rely on fundamentally limited modeling techniques and exotic, unscalable hardware. In this work, we address these shortcomings by proposing an all-transistor probabilistic computer that implements powerful denoising models at the hardware level. A system-level analysis indicates that devices based on our architecture could achieve performance parity with GPUs on a simple image benchmark using ~10,000 times less energy.
Fields of Study & Subjects
Advanced Memory and Neural Computing
Low-power high-performance VLSI design
Error Correcting Code Techniques
Publication details
- Journal / source
- npj Unconventional Computing
- Publisher
- Springer Science and Business Media LLC
- Volume
- 3
- Issue
- 1
- Publication date
- 2 July 2026
- Language
- EN