activity
20202025
most citedProbabilistic Neural Circuits leveraging AI-Enhanced Codesign for Random Number Generation

2 citations · 5 across the 6 of their papers we have counts for

collaborators

6 papers

cs.ET2025

A Bio-inspired Asymmetric Double-Gate Ferroelectric FET for Emulating Astrocyte and Dendrite Dynamics in Neuromorphic Systems

Zhouhang Jiang, A N M Nafiul Islam, Zhuangyu Han +13

Neuromorphic systems seek to replicate the functionalities of biological neural networks to attain significant improvements in performance and efficiency of AI computing platforms.…

cs.ET2024★ 1 cited

AI-Guided Codesign Framework for Novel Material and Device Design applied to MTJ-based True Random Number Generators

Karan P. Patel, Andrew Maicke, Jared Arzate +6

Novel devices and novel computing paradigms are key for energy efficient, performant future computing systems. However, designing devices for new applications is often time consumi…

cond-mat.mes-hall2023

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…

cs.ET2022★ 2 cited

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…

cond-mat.mes-hall2022★ 2 cited

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…

cs.NE2020

Evaluating complexity and resilience trade-offs in emerging memory inference machines

Christopher H. Bennett, Ryan Dellana, T. Patrick Xiao +6

Neuromorphic-style inference only works well if limited hardware resources are maximized properly, e.g. accuracy continues to scale with parameters and complexity in the face of po…