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<p>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.</p>

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An efficient probabilistic hardware architecture for diffusion-like models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月2日

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  • Andraž Jelinčič,
  • Owen Lockwood,
  • Akhil Garlapati,
  • Peter Schillinger,
  • Isaac L. Chuang,
  • Guillaume Verdon,
  • Trevor McCourt

摘要

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.