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Edge computing solutions that enable the extraction of high level informationfrom a variety of sensors is in increasingly high demand. This is due to theincreasing number of smart devices that require sensory processing for theirapplication on the edge. To tackle this problem, we present a smart visionsensor System on Chip (Soc), featuring an event-based camera and a low powerasynchronous spiking Convolutional Neuronal Network (sCNN) computingarchitecture embedded on a single chip. By combining both sensor and processingon a single die, we can lower unit production costs significantly. Moreover,the simple end-to-end nature of the SoC facilitates small stand-aloneapplications as well as functioning as an edge node in a larger systems. Theevent-driven nature of the vision sensor delivers high-speed signals in asparse data stream. This is reflected in the processing pipeline, focuses onoptimising highly sparse computation and minimising latency for 9 sCNN layersto $3.36\mu s$. Overall, this results in an extremely low-latency visualprocessing pipeline deployed on a small form factor with a low energy budgetand sensor cost. We present the asynchronous architecture, the individualblocks, the sCNN processing principle and benchmark against other sCNN capableprocessors.

Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline