Device Codesign Using Reinforcement Learning and Evolutionary Optimization
Abstract
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.
Cite
Text
Schuman et al. "Device Codesign Using Reinforcement Learning and Evolutionary Optimization." NeurIPS 2023 Workshops: MLNCP, 2023.Markdown
[Schuman et al. "Device Codesign Using Reinforcement Learning and Evolutionary Optimization." NeurIPS 2023 Workshops: MLNCP, 2023.](https://mlanthology.org/neuripsw/2023/schuman2023neuripsw-device/)BibTeX
@inproceedings{schuman2023neuripsw-device,
title = {{Device Codesign Using Reinforcement Learning and Evolutionary Optimization}},
author = {Schuman, Catherine and Cardwell, Suma G and Patel, Karan P. and Smith, J. Darby and Arzate, Jared and Maicke, Andrew and Liu, Samuel and Kwon, Jaesuk and Incorvia, Jean Anne},
booktitle = {NeurIPS 2023 Workshops: MLNCP},
year = {2023},
url = {https://mlanthology.org/neuripsw/2023/schuman2023neuripsw-device/}
}