10 citations · 17 across the 11 of their papers we have counts for
13 papers
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…
SANTA++: Sampling Attention through Representative Keys
Kyle Lee, Christian Z. Pratt, Ruoyu Fang +4
Attention often concentrates on a small subset of tokens in the context, but which subset matters changes from one query to the next. To exploit this changing structure, we introdu…
Extreme-Scale Ising Machines with Cluster Mean-Field Theory
Xiuqi Zhang, Shuvro Chowdhury, Shaila Niazi +3
Scaling analog and digital Ising machines to larger problems requires overcoming finite device capacity and the cost of communication between devices. We present cluster mean-field…
A 28nm 27,648-Spin Multichip Digital Ising Accelerator with Pegasus Connectivity
Tong Wu, Atharva Raut, Ian Khor +10
We report a 28 nm four-chip Ising accelerator with 27,648 spins, degree-15 Pegasus connectivity, and 10b coefficients. Boundary-state streaming overlaps interchip transfers with sp…
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…
From Independent to Correlated Diffusion: Generalized Generative Modeling with Probabilistic Computers
Nihal Sanjay Singh, Mazdak Mohseni-Rajaee, Shaila Niazi +1
Diffusion models have emerged as a powerful framework for generative tasks in deep learning. They decompose generative modeling into two computational primitives: deterministic neu…
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…
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…
IsingFormer: Augmenting Parallel Tempering With Learned Proposals
Saleh Bunaiyan, Corentin Delacour, Shuvro Chowdhury +3
Generative models have been extensively used to accelerate MCMC mixing for sampling and optimization, but their effective integration with standard MCMC remains an open question. H…
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…