Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection

• URL: http://arxiv.org/abs/2506.13440v1
• Date: Mon, 16 Jun 2025 12:54:27 GMT
• Title: Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection
• Authors: Shenqi Wang, Yingfu Xu, Amirreza Yousefzadeh, Sherif Eissa, Henk Corporaal, Federico Corradi, Guangzhi Tang,
• Abstract summary: We propose the Sparse Event-based Efficient Detector (SEED) for efficient event-based object detection on neuromorphic processors.<n>We introduce sparse convolutional recurrent learning, which achieves over 92% activation sparsity in recurrent processing, vastly reducing the cost for reasoning on sparse event data.
• Score: 4.362139927929203
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Leveraging the high temporal resolution and dynamic range, object detection with event cameras can enhance the performance and safety of automotive and robotics applications in real-world scenarios. However, processing sparse event data requires compute-intensive convolutional recurrent units, complicating their integration into resource-constrained edge applications. Here, we propose the Sparse Event-based Efficient Detector (SEED) for efficient event-based object detection on neuromorphic processors. We introduce sparse convolutional recurrent learning, which achieves over 92% activation sparsity in recurrent processing, vastly reducing the cost for spatiotemporal reasoning on sparse event data. We validated our method on Prophesee's 1 Mpx and Gen1 event-based object detection datasets. Notably, SEED sets a new benchmark in computational efficiency for event-based object detection which requires long-term temporal learning. Compared to state-of-the-art methods, SEED significantly reduces synaptic operations while delivering higher or same-level mAP. Our hardware simulations showcase the critical role of SEED's hardware-aware design in achieving energy-efficient and low-latency neuromorphic processing.

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