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We demonstrate an interactive digit recognition system using a spiking Deep Neural Network (DNN) FPGA-based system connected to two event-driven sensors: a Dynamic Vision Sensor (DVS) and a Dynamic Audio Sensor (DAS). Sensor fusion is demonstrated on a digit classification task using a DNN trained on the MNIST dataset supplemented by assignment of a unique pure ton e for each digit.
The use of spiking neuromorphic sensors with state-of-art deep networks is currently an active area of research. Still relatively unexplored are the pre-processing steps needed to transform spikes from these sensors and the types of network architectures that can produce high-accuracy performance using these sensors. This paper discusses several methods for preprocessing the spiking data from these...
This paper presents a real-time multi-modal spiking Deep Neural Network (DNN) implemented on an FPGA platform. The hardware DNN system, called n-Minitaur, demonstrates a 4-fold improvement in computational speed over the previous DNN FPGA system. The proposed system directly interfaces two different event-based sensors: a Dynamic Vision Sensor (DVS) and a Dynamic Audio Sensor (DAS). The DNN for this...
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