Abstract
The growing popularity of big data and machine learning applications call for a more powerful and energy-efficient way to execute deep learning workflows. Neuromorphic chips provide a potential solution, as they attempt to mimic the neuronal architectures in human brain and show great potentials in reducing energy consumption in the order of magnitude and also improve the computational performance. However, the fabrication process for neuromorphic chips is costly and currently based on trial-and-error, which adds complexity to the design process. In this paper, we address this challenge by designing and developing machine learning guided microfabrication process for Resistive Random Access Memory (RRAM), which is a key device in neuromorphic chips. Experimental results show that our approach is effective in terms of predicting the performance of RRAM devices fabricated under various process conditions.
Recommended Citation
A. Y. Vicenciodelmoral et al., "Supporting Green Neuromorphic Computing: Machine Learning Guided Microfabrication for Resistive Random Access Memory," Proceedings 2022 IEEE ACM 9th International Conference on Big Data Computing Applications and Technologies Bdcat 2022, pp. 154 - 157, Institute of Electrical and Electronics Engineers, Jan 2022.
The definitive version is available at https://doi.org/10.1109/BDCAT56447.2022.00026
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Big Data; Machine Learning; Microfabrication; Neuromorphic Computing; Resistive Random Access Memory
International Standard Book Number (ISBN)
978-166546090-3
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2022
Comments
National Science Foundation, Grant ECCS-2104976