RN-Net: Reservoir Nodes-Enabled Neuromorphic Vision Sensing Network
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Event-based cameras are inspired by the sparse and asynchronous spike representation of the biological visual system. However, processing the even data requires either using expensive feature descriptors to transform spikes into frames, or using spiking neural networks that are difficult to train. In this work, we propose a neural network architecture based on simple convolution layers integrated with dynamic temporal encoding reservoirs with low hardware and training costs. The Reservoir Nodes-enabled neuromorphic vision sensing Network (RN-Net) allows the network to efficiently process asynchronous temporal features, and achieves the highest accuracy of 99.2 Gesture reported to date, and one of the highest accuracy of 67.5 dataset at a much smaller network size. By leveraging the internal dynamics of memristors, asynchronous temporal feature encoding can be implemented at very low hardware cost without preprocessing or dedicated memory and arithmetic units. The use of simple DNN blocks and backpropagation based training rules further reduces its implementation cost. Code will be publicly available.
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