Laboratory for Low Power VLSI Design and Machine Learning Hardware Architecture
Research Interests
- Energy Efficient Computing
- AI/ML
- AI Hardware
- Flexible Electronics
- AI/ML Workload Analysis and Characterization
- Computer Architecture and Performance Evaluation
- Low Power VLSI Design
- Power-Aware and Secure Systems
Current Funded Research Projects
- Semiconductor Research Corporation (SRC), “Ultra Low-Energy Ultra Low-Latency Machine Learning using Weightless Neural Networks”, PI (100%), Jan 2023 – Dec 2025, $100,000.
- Semiconductor Research Corporation (SRC), “Machine Learning Workload Analysis and Characterization”, PI (100%), Jan 2021 – Dec 2023, $109,500.
- ARL, “Distributed Learning and Control for Multi-Domain, Multi-Modal, and Non-Stationary Data” , Sep 2022 – Sep 2023, $66,923.
- National Science Foundation (NSF), “EAGER: Exploring Artificial Intelligence Techniques for Energy-Efficient Arrhythmia Detection and Identification in Connected Implantable Cardiac Devices”, PI, Oct 2020-Sep 2022, $286,000.
- National Institute of Health (NIH), “Ultra Low Power Computing for Next Generation Implantable Smart Cardiac Pacemakers”, PI (100%), Feb 2018 – Feb 2023, $441,000.
Completed Research Support by:
- National Science Foundation (NSF)
- National Institute of Health (NIH)
- Army Research Office (ARO)
- Texas Higher Education Coordinating Board (THECB-ATP)
- Air Force Office of Scientific Research (AFOSR)
- Center for Infrastructure Assurance and Security (CIAS) at UT San Antonio
- INTEL Corporation
- IBM Corporation
- Xilinx