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Found 4 papers, 0 papers with code
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.
Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions
The increasing scale of neural networks and their growing application space have produced demand for more energy- and memory-efficient artificial-intelligence-specific hardware.
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).
