An Energy-Efficient Seizure Detection Processor using Event-Driven Multi-Stage CNN Classification and Segmented Data Processing with Adaptive Channel Selection
TimeTuesday, July 12th1:53pm - 2:15pm PDT
Location3002, Level 3
Event Type
Research Manuscript
AI/ML Design: Circuits and Architecture
DescriptionIn this work, an energy-efficient seizure detection processor is proposed, featuring multi-stage CNN classification, segmented data processing and adaptive channel selection to reduce the energy consumption while achieving high accuracy. The design has been fabricated and tested using a 28nm process technology. Compared with several state-of-the-art designs, the proposed design achieves the lowest energy per classification (1.48 μJ) with high sensitivity (97.78%) and low false positive rate per hour (0.5).