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Talk · IEEE International Conference on Artificial Intelligence Circuits and Systems · April 22, 2024

End-to-End Edge Neuromorphic Object Detection System

D. A. Silva, Ayan Shymyrbay, Kamilya Smagulova, A. Elsheikh, Mohammed E. Fouda, Ahmed M. Eltawil

Kamilya Smagulova
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Abstract

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.

Advanced Memory and Neural ComputingAdvanced Neural Network ApplicationsAdvanced Vision and Imagingneuromorphic computingspiking neural networksevent cameras

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