MulPi: A Multi-class and Patient-Independent Epileptic Seizure Classifier With Co-Designed Input-stationary Computing-in-SRAM

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 req...

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Bibliographic Details
Published in:IEEE transactions on biomedical circuits and systems Vol. 19; no. 4; pp. 756 - 766
Main Authors: Kim, Bokyung, Huang, Qijia, Taylor, Brady, Zheng, Qilin, Ku, Jonathan, Chen, Yiran, Li, Hai
Format: Journal Article
Language:English
Published: United States IEEE 01.08.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Algorithms
Artificial neural networks
Biological system modeling
Brain modeling
Chips (memory devices)
Classification
Classification algorithms
Co-design
Computation
Computer architecture
computing-in-memory
Convulsions & seizures
cross-patient
diagnosis automation
EEG
Electroencephalography
Epilepsy
Epilepsy - classification
Epilepsy - diagnosis
Fast Fourier transforms
Hardware
Humans
input-stationary dataflow
Multiclass seizure
Neural networks
Neural Networks, Computer
neural processor
patient-independence
Quality of life
Quantization (signal)
seizure classification
seizure detection
seizure prediction
Seizures
Seizures - classification
Seizures - diagnosis
Signal Processing, Computer-Assisted
SRAM-CIM
Static random access memory
Training
ISSN:1932-4545, 1940-9990, 1940-9990
Online Access:Get full text
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Summary: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<inline-formula><tex-math notation="LaTeX">{}^{2}</tex-math></inline-formula>.
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ISSN:1932-4545
1940-9990
1940-9990
DOI:10.1109/TBCAS.2025.3579273