136 citations · 367 across the 19 of their papers we have counts for
6 papers · 1 filter
Probabilistic approximate optimization using single-photon avalanche diode arrays
Ziyad Alswaidan, Abdelrahman S. Abdelrahman, Md Sakibur Sajal +9
Combinatorial optimization problems are central to science and engineering and specialized hardware from quantum annealers to classical Ising machines are being actively developed…
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…
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…
Machine Learning Quantum Systems with Magnetic p-bits
Shuvro Chowdhury, Kerem Y. Camsari
The slowing down of Moore's Law has led to a crisis as the computing workloads of Artificial Intelligence (AI) algorithms continue skyrocketing. There is an urgent need for scalabl…
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…