Implementation of a Retina Model on the SCAMP-5 Vision Sensor
Abstract
Standard Dynamic Vision Sensors (DVS) approximate retinal processing by detecting temporal contrast, offering high speed and dynamic range but omitting key biological mechanisms such as spatial filtering and contrast gain control. We present the first implementation of a multi-stage silicon retina model on the SCAMP-5 Pixel Processor Array (PPA), incorporating center-surround filtering, contrast gain control, and Leaky Integrate-and-Fire spiking directly at the focal plane. To enable broader research, we also provide a GPU-based simulation framework. Evaluating on video saliency prediction, the retina model achieves a 13% lower validation loss than a standard DVS baseline while generating 47% fewer events, demonstrating that biological pre-filtering can produce more efficient representations for downstream semantic tasks.