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Deep Neural Network Mapping and Performance Analysis on Tiled RRAM Architecture

Representative deep neural networks (DNNs) have been successfully mapped on an RRAM-based tiled in-memory computing (IMC) architecture. Effects of finite array size and quantized partial products (PPs) due to ADC precision constraints have been analyzed. Methods were developed to solve these challenges and preserve DNN accuracies and IMC performance gains in the tiled architecture. Popular models VGG-16, MobileNet, and RNN/LSTM have been successfully implemented and tested on ImageNet dataset and text classification tasks, respectively.