367 citations · 3.1k across the 70 of their papers we have counts for
70 papers
Spin Vector Control for Heisenberg-Inspired Probabilistic Computing
Yuanqiu Tan, Rahul Tripathi, Saleh Bunaiyan +5
Probabilistic bits (p-bits) have emerged as a cornerstone of probabilistic computing, enabling energy-efficient hardware implementation for probabilistic inference and combinatoria…
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…
Configurable p-Neurons Using Modular p-Bits
Saleh Bunaiyan, Mohammad Alsharif, Abdelrahman S. Abdelrahman +5
Probabilistic bits (p-bits) have recently been employed in neural networks (NNs) as stochastic neurons with sigmoidal probabilistic activation functions. Nonetheless, there remain…
Probabilistic Computers for Neural Quantum States
Shuvro Chowdhury, Jasper Pieterse, Navid Anjum Aadit +3
Neural quantum states efficiently represent many-body wavefunctions with neural networks, but the cost of Monte Carlo sampling limits their scaling to large system sizes. Here we a…
Metrics for spin-based computing
Hidekazu Kurebayashi, Giovanni Finocchio, Karin Everschor-Sitte +10
Spin-based computing is emerging as a powerful approach for energy-efficient and high-performance solutions to future data processing hardware. Spintronic devices function by elect…
Solving the compute crisis with physics-based ASICs
Maxwell Aifer, Zach Belateche, Suraj Bramhavar +11
Escalating artificial intelligence (AI) demands expose a critical "compute crisis" characterized by unsustainable energy consumption, prohibitive training costs, and the approachin…
Generalized Probabilistic Approximate Optimization Algorithm
Abdelrahman S. Abdelrahman, Shuvro Chowdhury, Flaviano Morone +1
We introduce a generalized \textit{Probabilistic Approximate Optimization Algorithm (PAOA)}, a classical variational Monte Carlo framework that extends and formalizes prior work by…
A CMOS Probabilistic Computing Chip With In-situ hardware Aware Learning
Jinesh Jhonsa, William Whitehead, David McCarthy +3
This paper demonstrates a probabilistic bit physics inspired solver with 440 spins configured in a Chimera graph, occupying an area of 0.44 mm^2. Area efficiency is maximized throu…
Pushing the Boundary of Quantum Advantage in Hard Combinatorial Optimization with Probabilistic Computers
Shuvro Chowdhury, Navid Anjum Aadit, Andrea Grimaldi +12
Recent demonstrations on specialized benchmarks have reignited excitement for quantum computers, yet whether they can deliver an advantage for practical real-world problems remains…
Integrated probabilistic computer using voltage-controlled magnetic tunnel junctions as its entropy source
Christian Duffee, Jordan Athas, Yixin Shao +6
Probabilistic Ising machines (PIMs) provide a path to solving many computationally hard problems more efficiently than deterministic algorithms on von Neumann computers. Stochastic…
How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits
Masoud Mohseni, Artur Scherer, K. Grace Johnson +48
In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become po…
Next-generation Probabilistic Computing Hardware with 3D MOSAICs, Illusion Scale-up, and Co-design
Tathagata Srimani, Robert Radway, Masoud Mohseni +2
The vast majority of 21st century AI workloads are based on gradient-based deterministic algorithms such as backpropagation. One of the key reasons for the dominance of determinist…
Noise-augmented Chaotic Ising Machines for Combinatorial Optimization and Sampling
Kyle Lee, Shuvro Chowdhury, Kerem Y. Camsari
Ising machines, hardware accelerators for combinatorial optimization and probabilistic sampling problems, have gained significant interest recently. A key element is stochasticity,…
Voltage-insensitive stochastic magnetic tunnel junctions with double free layers
Rikuto Ota, Keito Kobayashi, Keisuke Hayakawa +4
Stochastic magnetic tunnel junctions (s-MTJ) is a promising component of probabilistic bit (p-bit), which plays a pivotal role in probabilistic computers. For a standard cell struc…
Markov Chain Monte Carlo for Koopman-based Optimal Control: Technical Report
João Hespanha, Kerem Camsari
We propose a Markov Chain Monte Carlo (MCMC) algorithm based on Gibbs sampling with parallel tempering to solve nonlinear optimal control problems. The algorithm is applicable to n…
Connecting physics to systems with modular spin-circuits
Kemal Selcuk, Saleh Bunaiyan, Nihal Sanjay Singh +5
An emerging paradigm in modern electronics is that of CMOS + requiring the integration of standard CMOS technology with novel materials and technologies denoted by .…
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…
All-to-all reconfigurability with sparse and higher-order Ising machines
Srijan Nikhar, Sidharth Kannan, Navid Anjum Aadit +2
Domain-specific hardware to solve computationally hard optimization problems has generated tremendous excitement. Here, we evaluate probabilistic bit (p-bit) based Ising Machines (…
Heisenberg machines with programmable spin-circuits
Saleh Bunaiyan, Supriyo Datta, Kerem Y. Camsari
We show that we can harness two recent experimental developments to build a compact hardware emulator for the classical Heisenberg model in statistical physics. The first is the de…
Double-Free-Layer Stochastic Magnetic Tunnel Junctions with Synthetic Antiferromagnets
Kemal Selcuk, Shun Kanai, Rikuto Ota +3
Stochastic magnetic tunnel junctions (sMTJ) using low-barrier nanomagnets have shown promise as fast, energy-efficient, and scalable building blocks for probabilistic computing. De…
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…
Evaluating spintronics-compatible implementations of Ising machines
Andrea Grimaldi, Luciano Mazza, Eleonora Raimondo +7
The commercial and industrial demand for the solution of hard combinatorial optimization problems push forward the development of efficient solvers. One of them is the Ising machin…
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…