Area of research
Hardware and Architecture · Computer Vision and Pattern Recognition
Research interest
Research interests include Computer science, Benchmarking, Scalability, Artificial neural network, Throughput, and Speedup.
The neurobench framework for benchmarking neuromorphic computing algorithms and systems
International Retrospective Observational Study of Continual Learning for AI on Endotracheal Tube Placement from Chest Radiographs
RobotPerf: An Open-Source, Vendor-Agnostic, Benchmarking Suite for Evaluating Robotics Computing System Performance
GPU-based Private Information Retrieval for On-Device Machine Learning Inference
Federated benchmarking of medical artificial intelligence with MedPerf
An Electro-Photonic System for Accelerating Deep Neural Networks
RoboShape: Using Topology Patterns to Scalably and Flexibly Deploy Accelerators Across Robots
Deep Reinforcement Learning for Cyber Security
Algorithm-Hardware Co-Design of Adaptive Floating-Point Encodings for Resilient Deep Learning Inference