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It has been more than two decades since the first neuromorphic Dynamic Vision Sensor (DVS) sensor was invented, and many subsequent prototypes have been built with a wide spectrum of applications in mind. Competing against state-of-the-art neural networks in terms of accuracy is difficult, although there are clear opportunities to outperform conventional approaches in terms of power consumption and processing speed. As neuromorphic sensors generate sparse data at the focal plane itself, they are inherently energy-efficient, data-driven, and fast. In this work, we present an extended DVS pixel simulator for neuromorphic benchmarks which simplifies the latency and the noise models. In addition, to more closely model the behaviour of a real pixel, the readout circuitry is modelled, as this can strongly affect the time precision of events in complex scenes. Using a dynamic variant of the MNIST dataset as a benchmarking task, we use this simulator to explore how the latency of the sensor allows it to outperform conventional sensors in terms of sensing speed.

Event Camera Simulator Improvements via Characterized Parameters