RRAM based neuromorphic algorithms
arXiv:1903.02519
Abstract
This submission is a report on RRAM based neuromorphic algorithms. This report basically gives an overview of the algorithms implemented on neuromorphic hardware with crossbar array of RRAM synapses. This report mainly talks about the work on deep neural network to spiking neural network conversion and its significance.
12 pages, 6 figures, A version of this survey report is submitting as Springer book chapter
References in corpus (6)
- DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
- Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing
- Spiking Deep Networks with LIF Neurons
- Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks
- A Neuromorphic VLSI Design for Spike Timing and Rate Based Synaptic Plasticity
- Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks