MulPi: A Multi-class and Patient-Independent Epileptic Seizure Classifier With Co-Designed Input-stationary
Abstract:
Unprovoked seizures have threatened epilepsy patients over 70 million. Automated classification to detect and predict seizures could bring seizure-free lives to epilepsy patients, delivering them from fatal danger and increasing the quality of life. Authentic detection and prediction of seizures require 1) multi-class (Mul) and 2) patient-independent (Pi) classification. Recent implementable chips for seizure classification rarely satisfy the two requirements due to restricted resources in small chips; therefore, high efficiency is imperative along with accuracy. This paper introduces an efficient MulPi chip, fabricated for the first time to simultaneously fulfill multi-class and patient independence, based on a co-design approach. We develop a 5-layer convolutional neural network (CNN), MulPiCNN, with advanced training techniques for lightness and accuracy. At the hardware level, our SRAM-based chip leverages computing-in-memory (CIM) for efficiency. The fabricated MulPi chip is distinguished from prior CIMs in two folds, namely ISRW-CIM: a) input-stationary (IS) CIM for resource-saving, and b) row-wise (RW) computing to address a challenge of SRAM CIM, empowered by our novel 2T-Hadamard product unit (HPU). MulPi outperforms state-of-the-art chips with 98.5% sensitivity and 99.2% specificity, classifying in 0.12s and 0.348mm${}^{2}$.
