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Layer Ensemble Averaging for Improving Memristor-Based Artificial Neural Network Performance
Results demonstrate that by trading off the number of devices required for layer mapping, layer ensemble averaging can reliably boost defective memristive network performance up to the software baseline.
Experimental demonstration of a robust training method for strongly defective neuromorphic hardware
Methods such as hardware-aware training, where substrate non-idealities are incorporated during network training, are one way to recover performance at the cost of solution generality.
Device Modeling Bias in ReRAM-based Neural Network Simulations
Data-driven modeling approaches such as jump tables are promising techniques to model populations of resistive random-access memory (ReRAM) or other emerging memory devices for hardware neural network simulations.
Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks
In cloud and edge computing models, it is important that compute devices at the edge be as power efficient as possible.
