136 citations · 270 across the 6 of their papers we have counts for
6 papers
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…
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…
Mean-Field Assisted Deep Boltzmann Learning with Probabilistic Computers
Shuvro Chowdhury, Shaila Niazi, Kerem Y. Camsari
Despite their appeal as physics-inspired, energy-based and generative nature, general Boltzmann Machines (BM) are considered intractable to train. This belief led to simplified mod…
CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning
Nihal Sanjay Singh, Keito Kobayashi, Qixuan Cao +8
Extending Moore's law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. One important c…
A full-stack view of probabilistic computing with p-bits: devices, architectures and algorithms
Shuvro Chowdhury, Andrea Grimaldi, Navid Anjum Aadit +9
The transistor celebrated its 75 birthday in 2022. The continued scaling of the transistor defined by Moore's Law continues, albeit at a slower pace. Meanwhile, compu…
Training Deep Boltzmann Networks with Sparse Ising Machines
Shaila Niazi, Navid Anjum Aadit, Masoud Mohseni +3
The slowing down of Moore's law has driven the development of unconventional computing paradigms, such as specialized Ising machines tailored to solve combinatorial optimization pr…