96 citations · 260 across the 26 of their papers we have counts for
5 papers · 1 filter
Probabilistic Neural Circuits leveraging AI-Enhanced Codesign for Random Number Generation
Suma G. Cardwell, Catherine D. Schuman, J. Darby Smith +7
Stochasticity is ubiquitous in the world around us. However, our predominant computing paradigm is deterministic. Random number generation (RNG) can be a computationally inefficien…
Random Bitstream Generation using Voltage-Controlled Magnetic Anisotropy and Spin Orbit Torque Magnetic Tunnel Junctions
Samuel Liu, Jaesuk Kwon, Paul W. Bessler +6
Probabilistic computing using random number generators (RNGs) can leverage the inherent stochasticity of nanodevices for system-level benefits. The magnetic tunnel junction (MTJ) h…
Stochastic Neuromorphic Circuits for Solving MAXCUT
Bradley H. Theilman, Yipu Wang, Ojas D. Parekh +3
Finding the maximum cut of a graph (MAXCUT) is a classic optimization problem that has motivated parallel algorithm development. While approximate algorithms to MAXCUT offer attrac…
Sequence Learning and Consolidation on Loihi using On-chip Plasticity
Jack Lindsey, James B Aimone
In this work we develop a model of predictive learning on neuromorphic hardware. Our model uses the on-chip plasticity capabilities of the Loihi chip to remember observed sequences…
Spiking Neural Streaming Binary Arithmetic
James B. Aimone, Aaron J. Hill, William M. Severa +1
Boolean functions and binary arithmetic operations are central to standard computing paradigms. Accordingly, many advances in computing have focused upon how to make these operatio…