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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.
Biologically-Informed Excitatory and Inhibitory Balance for Robust Spiking Neural Network Training
Spiking neural networks drawing inspiration from biological constraints of the brain promise an energy-efficient paradigm for artificial intelligence.
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.
Memory-efficient training with streaming dimensionality reduction
The movement of large quantities of data during the training of a Deep Neural Network presents immense challenges for machine learning workloads.
Streaming Batch Eigenupdates for Hardware Neuromorphic Networks
Neuromorphic networks based on nanodevices, such as metal oxide memristors, phase change memories, and flash memory cells, have generated considerable interest for their increased energy efficiency and density in comparison to graphics processing units (GPUs) and central processing units (CPUs).
