3 papers
cs.LG2026
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…
cond-mat.mes-hall2024
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…
cs.ET2024
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…