11 citations · 12 across the 6 of their papers we have counts for
5 papers · 1 filter
Probabilistic Algorithms for Ising Machines from Optimization to Generative AI
Corentin Delacour, Xiuqi Zhang, Abdelrahman S. Abdelrahman +4
Ising machines have emerged as promising hardware accelerators for intractable optimization and sampling problems, yet their practical impact increasingly hinges on the co-design o…
Programmable Probabilistic Computer with 1,000,000 p-bits
Navid Anjum Aadit, Xiuqi Zhang, Shuvro Chowdhury +10
Probabilistic computers built from p-bits have been proposed as hardware accelerators for sampling and optimizing Ising models, but existing systems have been confined to a single…
Stochastic Sparse Attention for Memory-Bound Inference
Kyle Lee, Corentin Delacour, Kevin Callahan-Coray +5
Autoregressive decoding becomes bandwidth-limited at long contexts, as generating each token requires reading all key and value vectors from KV cache. We present Stochastic A…
Breakdown of Gradient-Flow Dynamics in Oscillator Ising Machines from Harmonic Misalignment
Abir Hasan, E. M. Hasantha Ekanayake, Kyle Lee +2
Oscillator Ising machines (OIMs) are often viewed as physical systems that perform gradient descent on an energy landscape encoding Ising solutions. Here, we show that this interpr…
Restoring Sparsity in Potts Machines via Mean-Field Constraints
Kevin Callahan-Coray, Kyle Lee, Kyle Jiang +1
Ising machines and related probabilistic hardware have emerged as promising platforms for NP-hard optimization and sampling. However, many practical problems involve constraints th…