This demo shows a neuromorphic computing system called INsight that classifies images fed by an OV7670 camera in real-time into 10 categories such as dogs, cats, trucks, etc. A 6-layer deep convolutional neural network with 0.7M parameters is trained using the training set of CIFAR-10, and it is compressed and converted into a time delay neural network (TDNN) with 18,000 connections. Then, the TDNN is mapped one-to-one into silicon neurons and synapses on INsight implemented with Xilinx Kintex 7 325T FPGA. This system achieves 80.23% accuracy for the test set of CIFAR-10 and can process 4882 images per second consuming 2.14W power.
['Taehwan Shin', 'Yongshin Kang', 'Seungho Yang', 'Seban Kim', 'Jaeyong Chung']
Live demonstration: Real-time image classification on a neuromorphic computing system with zero off-chip memory access