Espresso: Exploiting the Sparsity Property in Event Sensors with Spatiotemporal Ordering

Event-based vision sensors are novel cameras inspired by human eyes, capable of capturing the rapid motion of objects with the high-speed sparse event stream. However, it is challenging to efficiently stream process event data without demolishing its sparsity. In this paper, we design Espresso, an architecture for event-based vision processing that can efficiently stream spatiotemporal events while preserving sparsity. We implement Espresso on the FPGA platform and design experiments to compare the performance with embedded GPU and line-buffer-based accelerator. In real-world scenarios, Espresso achieves throughput up to 5000 fps, which is $5.1 \times$ higher than the embedded GPU.