An efficient probabilistic hardware architecture for diffusion-like models

This is experimental HTML to improve accessibility. We invite you to report rendering errors. Use Alt+Y to toggle on accessible reporting links and Alt+Shift+Y to toggle off. Learn more about this project and help improve conversions. The proliferation of probabilistic AI has promoted 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 approximately 10,000 times less energy. The unprecedented recent investment in large-scale AI systems will soon put a strain on the world’s energy infrastructure. Every year,

† † thanks: These authors contributed equally to this work. An efficient probabilistic hardware architecture for diffusion-like models Andraž Jelinčič Owen Lockwood Akhil Garlapati Guillaume Verdon Trevor McCourt ∗ , \,{}^{*,\,} trevor@extropic.ai Extropic Corporation (October 28, 2025) Abstract The proliferation of probabilistic AI has promoted 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 th

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