Device Codesign using Reinforcement Learning

SG Cardwell, K Patel, CD Schuman… - … on Circuits and …, 2024 - ieeexplore.ieee.org
We demonstrate device codesign using reinforcement learning for probabilistic computing
applications. We use a spin orbit torque magnetic tunnel junction model (SOT-MTJ) as the …

Stoch-IMC: A bit-parallel stochastic in-memory computing architecture based on STT-MRAM

AM Hajisadeghi, HR Zarandi, M Momtazpour - AEU-International Journal of …, 2025 - Elsevier
In-memory computing (IMC) offloads parts of the computations to memory to fulfill the
performance and energy demands of applications such as neuromorphic computing …

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

KP Patel, A Maicke, J Arzate, J Kwon, JD Smith… - arxiv preprint arxiv …, 2024 - arxiv.org
Novel devices and novel computing paradigms are key for energy efficient, performant future
computing systems. However, designing devices for new applications is often time …

High-Speed Tunable Generation of Random Number Distributions Using Actuated Perpendicular Magnetic Tunnel Junctions

ASE Valli, M Tsao, JD Smith, S Misra… - arxiv preprint arxiv …, 2025 - arxiv.org
Perpendicular magnetic tunnel junctions (pMTJs) actuated by nanosecond pulses are
emerging as promising devices for true random number generation (TRNG) due to their …