@inproceedings{
schuman2023device,
title={Device Codesign using Reinforcement Learning and Evolutionary Optimization},
author={Catherine Schuman and Suma G Cardwell and Karan P. Patel and J. Darby Smith and Jared Arzate and Andrew Maicke and Samuel Liu and Jaesuk Kwon and Jean Anne Incorvia},
booktitle={Machine Learning with New Compute Paradigms},
year={2023},
url={https://openreview.net/forum?id=BtC95j83bY}
}
Device discovery and circuit modeling for emerging devices, such as magnetic tunnel junctions, require detailed and time-consuming device and circuit simulations. In this work, we propose using AI-guided techniques such as reinforcement learning and evolutionary optimization to accelerate device discovery, creativity of solutions, and automate optimization to design true random number generators for a given distribution. We present preliminary results designing true random number generators using magnetic tunnel junctions optimized for performance.
Device Codesign using Reinforcement Learning and Evolutionary Optimization
Catherine Schuman Suma G Cardwell Karan P. Patel J. Darby Smith Jared Arzate Andrew Maicke Samuel Liu Jaesuk Kwon Jean Anne Incorvia