2 citations · 4 across the 3 of their papers we have counts for
5 papers
Magnetic Tunnel Junction Random Number Generators Applied to Dynamically Tuned Probability Trees Driven by Spin Orbit Torque
Andrew Maicke, Jared Arzate, Samuel Liu +7
Perpendicular magnetic tunnel junction (pMTJ)-based true-random number generators (RNG) can consume orders of magnitude less energy per bit than CMOS pseudo-RNG. Here, we numerical…
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…
Controllable reset behavior in domain wall-magnetic tunnel junction artificial neurons for task-adaptable computation
Samuel Liu, Christopher H. Bennett, Joseph S. Friedman +3
Neuromorphic computing with spintronic devices has been of interest due to the limitations of CMOS-driven von Neumann computing. Domain wall-magnetic tunnel junction (DW-MTJ) devic…
Adaptive cognition implemented with a context-aware and flexible neuron for next-generation artificial intelligence
Priyamvada Jadaun, Can Cui, Sam Liu +1
Neuromorphic computing mimics the organizational principles of the brain in its quest to replicate the brain's intellectual abilities. An impressive ability of the brain is its ada…