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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Veröffentlicht in:	IEEE transactions on biomedical circuits and systems Jg. 19; H. 4; S. 756 - 766
Hauptverfasser:	Kim, Bokyung, Huang, Qijia, Taylor, Brady, Zheng, Qilin, Ku, Jonathan, Chen, Yiran, Li, Hai
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.08.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
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Zusammenfassung:	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>.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1932-4545 1940-9990 1940-9990
DOI:	10.1109/TBCAS.2025.3579273