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

• URL: http://arxiv.org/abs/2304.06793v2
• Date: Mon, 27 May 2024 09:06:35 GMT
• Title: Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline
• Authors: Ole Richter, Yannan Xing, Michele De Marchi, Carsten Nielsen, Merkourios Katsimpris, Roberto Cattaneo, Yudi Ren, Yalun Hu, Qian Liu, Sadique Sheik, Tugba Demirci, Ning Qiao,
• Abstract summary: We present a smart vision sensor System on Chip (SoC), featuring an event-based camera and a low-power asynchronous spiking Convolutional Neural Network (sCNN) computing architecture embedded on a single chip. By combining both sensor and processing on a single die, we can lower unit production costs significantly. We present the asynchronous architecture, the individual blocks, and the sCNN processing principle and benchmark against other sCNN capable processors.
• Score: 5.8859061623552975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Edge computing solutions that enable the extraction of high-level information from a variety of sensors is in increasingly high demand. This is due to the increasing number of smart devices that require sensory processing for their application on the edge. To tackle this problem, we present a smart vision sensor System on Chip (SoC), featuring an event-based camera and a low-power asynchronous spiking Convolutional Neural Network (sCNN) computing architecture embedded on a single chip. By combining both sensor and processing on a single die, we can lower unit production costs significantly. Moreover, the simple end-to-end nature of the SoC facilitates small stand-alone applications as well as functioning as an edge node in larger systems. The event-driven nature of the vision sensor delivers high-speed signals in a sparse data stream. This is reflected in the processing pipeline, which focuses on optimising highly sparse computation and minimising latency for 9 sCNN layers to 3.36{\mu}s for an incoming event. Overall, this results in an extremely low-latency visual processing pipeline deployed on a small form factor with a low energy budget and sensor cost. We present the asynchronous architecture, the individual blocks, and the sCNN processing principle and benchmark against other sCNN capable processors.

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