3 citations · 7 across the 7 of their papers we have counts for
7 papers
multi-Stochastic Core Architecture for Scaling Probabilistic Ising Machines
Chirag Garg, Pratik Brahma, Saavan Patel +1
Ising Machines offer vast potential to solve NP-hard optimization problems efficiently that are intractable to solve using conventional computing architecture. A lot of these optim…
Hardware Acceleration of Frustrated Lattice Systems using Convolutional Restricted Boltzmann Machine
Pratik Brahma, Junghoon Han, Tamzid Razzaque +2
Geometric frustration gives rise to emergent quantum phenomena and exotic phases of matter. While Monte Carlo methods are traditionally used to simulate such systems, their samplin…
PASS: An Asynchronous Probabilistic Processor for Next Generation Intelligence
Saavan Patel, Philip Canoza, Adhiraj Datar +3
New computing paradigms are required to solve the most challenging computational problems where no exact polynomial time solution exists.Probabilistic Ising Accelerators has gained…
Distributed On-Sensor Compute System for AR/VR Devices: A Semi-Analytical Simulation Framework for Power Estimation
Jorge Gomez, Saavan Patel, Syed Shakib Sarwar +8
Augmented Reality/Virtual Reality (AR/VR) glasses are widely foreseen as the next generation computing platform. AR/VR glasses are a complex "system of systems" which must satisfy…
Ising Model Optimization Problems on a FPGA Accelerated Restricted Boltzmann Machine
Saavan Patel, Lili Chen, Philip Canoza +1
Optimization problems, particularly NP-Hard Combinatorial Optimization problems, are some of the hardest computing problems with no known polynomial time algorithm existing. Recent…
Logically Synthesized, Hardware-Accelerated, Restricted Boltzmann Machines for Combinatorial Optimization and Integer Factorization
Saavan Patel, Philip Canoza, Sayeef Salahuddin
The Restricted Boltzmann Machine (RBM) is a stochastic neural network capable of solving a variety of difficult tasks such as NP-Hard combinatorial optimization problems and intege…