367 citations · 3.1k across the 70 of their papers we have counts for
24 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…
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…
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…
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…
Roadmap for Unconventional Computing with Nanotechnology
Giovanni Finocchio, Jean Anne C. Incorvia, Joseph S. Friedman +48
In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same ti…
CMOS-compatible Ising and Potts Annealing Using Single Photon Avalanche Diodes
William Whitehead, Zachary Nelson, Kerem Y. Camsari +1
Massively parallel annealing processors may offer superior performance for a wide range of sampling and optimization problems. A key component dictating the size of these processor…
Massively Parallel Probabilistic Computing with Sparse Ising Machines
Navid Anjum Aadit, Andrea Grimaldi, Mario Carpentieri +4
Inspired by the developments in quantum computing, building domain-specific classical hardware to solve computationally hard problems has received increasing attention. Here, by in…
Quantitative Evaluation of Hardware Binary Stochastic Neurons
Orchi Hassan, Supriyo Datta, Kerem Y. Camsari
Recently there has been increasing activity to build dedicated Ising Machines to accelerate the solution of combinatorial optimization problems by expressing these problems as a gr…
Hardware Design for Autonomous Bayesian Networks
Rafatul Faria, Jan Kaiser, Kerem Y. Camsari +1
Directed acyclic graphs or Bayesian networks that are popular in many AI related sectors for probabilistic inference and causal reasoning can be mapped to probabilistic circuits bu…
Probabilistic Circuits for Autonomous Learning: A simulation study
Jan Kaiser, Rafatul Faria, Kerem Y. Camsari +1
Modern machine learning is based on powerful algorithms running on digital computing platforms and there is great interest in accelerating the learning process and making it more e…
Autonomous Probabilistic Coprocessing with Petaflips per Second
Brian Sutton, Rafatul Faria, Lakshmi A. Ghantasala +3
In this paper we present a concrete design for a probabilistic (p-) computer based on a network of p-bits, robust classical entities fluctuating between -1 and +1, with probabiliti…
Low Barrier Magnet Design for Efficient Hardware Binary Stochastic Neurons
Orchi Hassan, Rafatul Faria, Kerem Y. Camsari +2
Binary stochastic neurons (BSN's) form an integral part of many machine learning algorithms, motivating the development of hardware accelerators for this complex function. It has b…
Analog Signal Processing Using Stochastic Magnets
Samiran Ganguly, Kerem Y. Camsari, Avik W. Ghosh
We present a low barrier magnet based compact hardware unit for analog stochastic neurons and demonstrate its use as a building-block for neuromorphic hardware. By coupling circula…
Composable Probabilistic Inference Networks Using MRAM-based Stochastic Neurons
Ramtin Zand, Kerem Y. Camsari, Supriyo Datta +1
Magnetoresistive random access memory (MRAM) technologies with thermally unstable nanomagnets are leveraged to develop an intrinsic stochastic neuron as a building block for restri…
p-Bits for Probabilistic Spin Logic
Kerem Y. Camsari, Brian M. Sutton, Supriyo Datta
We introduce the concept of a probabilistic or p-bit, intermediate between the standard bits of digital electronics and the emerging q-bits of quantum computing. We show that low b…
Voltage-driven Building Block for Hardware Belief Networks
Orchi Hassan, Kerem Y. Camsari, Supriyo Datta
Probabilistic spin logic (PSL), based on networks of binary stochastic neurons (or p-bits), has been shown to provide a viable framework for many functionalities including Ising co…
Implementing Bayesian Networks with Embedded Stochastic MRAM
Rafatul Faria, Kerem Y. Camsari, Supriyo Datta
Magnetic tunnel junctions (MTJ's) with low barrier magnets have been used to implement random number generators (RNG's) and it has recently been shown that such an MTJ connected to…
Weighted p-bits for FPGA implementation of probabilistic circuits
Ahmed Zeeshan Pervaiz, Brian M. Sutton, Lakshmi Anirudh Ghantasala +1
Probabilistic spin logic (PSL) is a recently proposed computing paradigm based on unstable stochastic units called probabilistic bits (p-bits) that can be correlated to form probab…
Low-Energy Deep Belief Networks using Intrinsic Sigmoidal Spintronic-based Probabilistic Neurons
Ramtin Zand, Kerem Yunus Camsari, Steven D. Pyle +3
A low-energy hardware implementation of deep belief network (DBN) architecture is developed using near-zero energy barrier probabilistic spin logic devices (p-bits), which are mode…
Reservoir Computing using Stochastic p-Bits
Samiran Ganguly, Kerem Y. Camsari, Avik W. Ghosh
We present a general hardware framework for building networks that directly implement Reservoir Computing, a popular software method for implementing and training Recurrent Neural…
Hardware emulation of stochastic p-bits for invertible logic
Ahmed Zeeshan Pervaiz, Lakshmi Anirudh Ghantasala, Kerem Yunus Camsari +1
The common feature of nearly all logic and memory devices is that they make use of stable units to represent 0's and 1's. A completely different paradigm is based on three-terminal…