Scholar
Tokey Tahmid
Google Scholar ID: SEXOtTgAAAAJ
Research Associate, Innovative Computing Laboratory, University of Tennessee Knoxville
Citations & Impact
Citations
7
H-index
2
i10-index
0
Publications
7
Co-authors
3
list available
Publications
Resume (English only)
Academic Achievements
- - "PAPI Support for Specialized AI Architectures" – SC2025 (PDSW’25 Workshop)
- - "SpikeRL: A Scalable and Energy-efficient Framework for Deep Spiking Reinforcement Learning" – ICONS2025
- - "Energy-Efficient Computing for Scalable and Sustainable AI" – University of Tennessee 2024
- - "Towards Scalable and Efficient Spiking Reinforcement Learning for Continuous Control Tasks" – ICONS2024
- - "Low Precision for Lower Energy Consumption" – ASCR Energy Efficient Workshop 2024
- - "Low Precision and Efficient Programming Languages for Sustainable AI: Final Report for the Summer Project of 2024" – National Renewable Energy Laboratory 2024
- - "Towards the FAIR Asset Tracking Across Models, Datasets, and Performance Evaluation Scenarios" – HPEC2023
- - "Character animation using reinforcement learning and imitation learning algorithms" – ICIEV and icIVPR 2021
Research Experience
- - Working in the Performance Measurement & Modeling group at ICL with Dr. Heike Jagode, developing PAPI software support for ever-growing AI chips and accelerators such as the Intel Habana Gaudi.
- - Research work during a summer internship at the National Renewable Energy Laboratory (NREL) with Dr. Weslley Da Silva Pereira, on the “Low Precision and Efficient Programming Languages for Sustainable AI” role, demonstrating excellent results (average speedup of 2.05X and 80.75% better energy efficiency) with mixed-precision training on multiple AI applications at NREL.
- - Master's thesis research on Neuromorphic applications and Spiking Neural Networks (SNN) with Dr. Catherine Schuman, developing a scalable and energy-efficient infrastructure (≈22% better and ≈39% more efficient than state-of-the-art) for deep reinforcement learning (DRL) based spiking neural networks (SNN) with MPI for distributed training and mixed precision for optimization.
Education
- Master's degree in Computer Science from the University of Tennessee, Knoxville, under the supervision of Dr. Catherine Schuman.
Background
- Research interests include High-Performance Computing, Performance Engineering, and Artificial Intelligence. He is a research associate at the Innovative Computing Laboratory (ICL) at the University of Tennessee, Knoxville, focusing on developing Performance Application Programming Interface (PAPI) support for specialized AI hardware and software.
Miscellany
- Feel free to reach out via email (tokeytahmid13@gmail.com).
