RISE Seminar - Accelerating Healthcare AI: Energy - Efficient Hardware Accelerators for Disease Detection

RISE Seminar: Accelerating Healthcare AI — Energy-Efficient Hardware for Disease Detection

Talk highlights include:

• AI-driven disease detection in medical imaging and physiological signal analysis

• The need for real-time, reliable inference in healthcare applications

• Key challenges: limited labeled medical data and strict power, memory, and latency constraints

• Algorithm–hardware co-design for low-power, real-time AI systems

• Lightweight neural models optimized for efficient hardware implementation

• Case study: ASIC-based accelerator for arrhythmia detection

• Future directions: extending energy-efficient designs to dementia, pneumonia, and other diseases

Ideal for faculty, students, and researchers interested in AI, healthcare technology, hardware design, and embedded systems.

Dr. Anshul Jha is an Assistant Professor at the University of the Pacific whose research focuses on energy-efficient AI hardware for healthcare. She develops low-power, real-time machine learning systems for medical imaging and biosignals, advancing deployable solutions for disease detection in edge and point-of-care settings.