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Found 4 papers, 0 papers with code
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.
Multiplexed gradient descent: Fast online training of modern datasets on hardware neural networks without backpropagation
We present multiplexed gradient descent (MGD), a gradient descent framework designed to easily train analog or digital neural networks in hardware.
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.
Multi-Criteria Radio Spectrum Sharing With Subspace-Based Pareto Tracing
We perform an exploratory analysis of coexistence behavior by approximating active subspaces to identify low-dimensional structure in the optimization criteria, i. e., few linear combinations of parameters for simultaneously maximizing LAA and Wi-Fi throughputs.
