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; especially 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 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 University 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 DL
accelerators. Furthermore, we investigate the effects of non-idealities on our
system and investigate Quantization Aware Training (QAT) to mitigate the
performance degradation due to low ADC/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 31.255mm$^2$ in a 22nm
FDSOI CMOS process.

本文提出一种新颖的低延迟并行卷积神经网络 (CNN) 框架，该框架使用阻性随机存取存储器 (RRAM) 器件来并行执行卷积层内核，从而实现了比当前最先进的 Memristive-CMOS 混合 DL 加速器低两个数量级的延迟降低，同时考虑非理想性，提出一种量化感知训练的方法以及一种卡住权重的偏移方法来增加系统性能。