Research

Laboratory for Low Energy Computing and Machine Learning

Research Interests

  • Energy Efficient Computing
  • AI/ML
  • AI Hardware
  • AI/ML Workload Analysis and Characterization
  • Computer Architecture and Performance Evaluation
  • Flexible Electronics
  • Low Power VLSI Design
  • Power-Aware and Secure Systems

Current and Recent Funded Research Projects

  • National Science Foundation (NSF), “FuSe2: Topic 1: Efficient Edge Inference and Heterogeneous Integration in Systems for Health and Chemical Sensing”,  Co-PI, Sep 2024 – Aug 2027, $1,715,924.00
  • 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