7 papers
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…
Probabilistic Computers for MIMO Detection: From Sparsification to 2D Parallel Tempering
M Mahmudul Hasan Sajeeb, Kevin Callahan-Coray, Corentin Delacour +3
Probabilistic computers built from p-bits offer a promising path for combinatorial optimization, but the dense connectivity required by real-world problems scales poorly in hardwar…
Lagrange Oscillatory Neural Networks for Constraint Satisfaction and Optimization
Corentin Delacour, Bram Haverkort, Filip Sabo +2
Physics-inspired computing paradigms are receiving renewed attention to enhance efficiency in compute-intensive tasks such as artificial intelligence and optimization. Similar to H…
IsingFormer: Augmenting Parallel Tempering With Learned Proposals
Saleh Bunaiyan, Corentin Delacour, Shuvro Chowdhury +2
Markov Chain Monte Carlo (MCMC) underlies both statistical physics and combinatorial optimization, but mixes slowly near critical points and in rough landscapes. Parallel Tempering…
Two-dimensional Parallel Tempering for Constrained Optimization
Corentin Delacour, M Mahmudul Hasan Sajeeb, Joao P. Hespanha +1
Sampling Boltzmann probability distributions plays a key role in machine learning and optimization, motivating the design of hardware accelerators such as Ising machines. While the…
Scalable Connectivity for Ising Machines: Dense to Sparse
M Mahmudul Hasan Sajeeb, Navid Anjum Aadit, Shuvro Chowdhury +7
In recent years, hardware implementations of Ising machines have emerged as a viable alternative to quantum computing for solving hard optimization problems among other application…