End-to-End Edge Neuromorphic Object Detection System

Neuromorphic accelerators are emerging as a potential solution to the growing power demands of Artificial Intelligence (AI) applications. Spiking Neural Networks (SNNs), which are bio-inspired architectures, are being considered as a way to address this issue. Neuromorphic cameras, which operate on a similar principle, have also been developed, offering low power consumption, microsecond latency, and robustness in various lighting conditions. This work presents a full neuromorphic system for Computer Vision, from the camera to the processing hardware, with a focus on object detection. The system was evaluated on a compiled real-world dataset and a new synthetic dataset generated from existing videos, and it demonstrated good performance in both cases. The system was able to make accurate predictions while consuming 66mW, with a sparsity of 83%, and a time response of 138ms.