Event-Driven Configurable Module with Refractory Mechanism for ConvNets on FPGA

Abstract

We have developed a fully configurable event-driven convolutional module with refractory period mechanism that can be used to implement arbitrary Convolutional Neural Networks (ConvNets) on FPGAs following a 2D array structure. Using this module, we have implemented in a Spartan6 FPGA a 4-layer ConvNet with 22 convolutional modules trained for poker card symbol recognition. It has been tested with a stimulus where 40 poker cards were observed by a Dynamic Vision Sensor (DVS) in 1s time. A traffic control mechanism is implemented to downsample high speed input stimuli while keeping spatio-temporal correlation. For slow stimulus play back, a 96% recognition rate is achieved with a power consumption of 0.85mW. At maximum play back speed, the recognition rate is still above 63% when less than 20% of the input events are processed.