The research will advance the development of AI systems to meet increasing computational demands, while achieving more human-like cognitive capabilities with improved efficiency.
Ph.D. candidate Fabia Farlin Athena received the Johns Hopkins Applied Physics Laboratory (APL) Machine Learning Outstanding Oral Presentation Award.
She was recently notified of the award for her presentation, “Describing the Analog Resistance Change of HfOx Neuromorphic Synapses,” given at the Materials Research Society’s (MRS) Fall 2023 Meeting in Boston.
The research was recognized for its contributions to future materials and technologies toward sustainable heterogeneous computing and energy-efficient machine learning.
MRS brings together materials researchers from around the world to promote the sharing and communication of interdisciplinary research and technology to improve the quality of life.
Athena, an IBM Ph.D. Fellow, conducted the research with her advisor, Eric M. Vogel, a Hightower Professor in the School of Materials Science and Engineering (MSE), with an adjunct appointment in School of Electrical and Computer Engineering (ECE).
She presented a compact model, Compact Series Trap-Assisted Tunneling and Ohmic conduction (C-STAO), which enables the rapid simulation of artificial HfOx synaptic devices. The model provides deep insights into the analog temporal responses and temperature dependency of artificial synaptic devices, which are used to develop brain-inspired circuits.
These brain-inspired circuits are important for the development of AI systems and can help them to meet increasing computational demands while achieving more human-like cognitive capabilities with improved efficiency.
It also furthers Athena’s Ph.D. research, dedicated to the development of adaptive oxide devices for neuromorphic computing, aimed at replicating the human brain's functionality for more intelligent and energy-efficient computing.
Funding Acknowledgement
This project was supported by the Air Force Office of Scientific Research MURI entitled, “Cross-disciplinary Electronic-ionic Research Enabling Biologically Realistic Autonomous Learning (CEREBRAL)” under Award No. FA9550-18-1-0024.
This work was performed in part at the Georgia Tech Institute for Electronics and Nanotechnology, a member of the National Nanotechnology Coordinated Infrastructure (NNCI), which is supported by the National Science Foundation (No. ECCS-1542174). The work was also supported by the Cadence Diversity in Technology Scholarship and the IBM Ph.D. Fellowship Award No. 2022-2024.