Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks
Corey Lammie a
a IBM Research – Zurich, Säumerstrasse, 4, Rüschlikon, Switzerland
Proceedings of Neuromorphic Materials, Devices, Circuits and Systems (NeuMatDeCaS)
València, Spain, 2023 January 23rd - 25th
Organizers: Rohit Abraham John, Irem Boybat, Jason Eshraghian and Simone Fabiano
Contributed talk, Corey Lammie, presentation 075
Publication date: 9th January 2023

During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware implementation using conventional technologies, such as Complementary Metal-Oxide-€“Semiconductor (CMOS), in power and area-constrained settings remains a challenging task; espe- cially when many recording channels are used. In this paper, we propose a novel low-latency parallel Convolutional Neural Network (CNN) architecture that has between 2-2,800x fewer network parameters compared to State-Of-The-Art (SOTA) CNN architectures and achieves 5-fold cross validation accuracy of 99.84% for epileptic seizure detection, and 99.01% and 97.54% for epileptic seizure prediction, when evaluated using the Uni- versity of Bonn Electroencephalogram (EEG), CHB-MIT and SWEC-ETHZ seizure datasets, respectively. We subsequently implement our network onto analog crossbar arrays comprising Resistive Random-Access Memory (RRAM) devices, and provide a comprehensive benchmark by simulating, laying out, and determining hardware requirements of the CNN component of our system. To the best of our knowledge, we are the first to parallelize the execution of convolution layer kernels on separate analog crossbars to enable 2 orders of magnitude reduction in latency compared to SOTA hybrid Memristive-CMOS Deep Learning (DL) accelerators. Furthermore, we investigate the ef- fects of non-idealities on our system and investigate Quantization Aware Training (QAT) to mitigate the performance degradation due to low Analog-to-Digital Converter (ADC)/Digital-to-Analog Converter (DAC) resolution. Finally, we propose a stuck weight offsetting methodology to mitigate performance degradation due to stuck RON/ROFF memristor weights, recovering up to 32% accuracy, without requiring retraining. The CNN component of our platform is estimated to consume approximately 2.791W of power while occupying an area of 31.255mm2 in a 22nm FDSOI CMOS process.

All authors of the original publication [1] are acknowledged. This work was performed during my PhD studies at James Cook University, Australia, under the supervision of Assistant Prof. Mostafa Rahimi Azhghadi.

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