The complexity of event-based object detection (OD) poses considerable challenges. Spiking Neural Networks (SNNs) show promising results and pave the way for efficient event-based OD. Despite this success, the path to efficient SNNs on embedded devices remains a challenge. This is due to the size of the networks required to accomplish the task and the ability of devices to take advantage of SNNs benefits. Even when "edge" devices are considered, they typically use embedded GPUs that consume tens of watts. In response to these challenges, our research introduces an embedded neuromorphic testbench that utilizes the SPiking Low-power Event-based ArchiTecture (SPLEAT) accelerator. Using an extended version of the Qualia framework, we can train, evaluate, quantize, and deploy spiking neural networks on an FPGA implementation of SPLEAT. We used this testbench to load a state-of-the-art SNN solution, estimate the performance loss associated with deploying the network on dedicated hardware, and run real-world event-based OD on neuromorphic hardware specifically designed for low-power spiking neural networks. Remarkably, our embedded spiking solution, which includes a model with 1.08 million parameters, operates efficiently with 490 mJ per prediction.

翻译：事件驱动的目标检测（OD）任务具有显著的复杂性挑战。脉冲神经网络（SNNs）在该领域展现出良好前景，为高效的事件驱动OD提供了可行路径。尽管取得了一定成功，但在嵌入式设备上实现高效SNN部署仍面临诸多挑战，这主要源于任务所需网络的规模以及设备利用SNN优势的能力限制。即使考虑"边缘"设备，其通常搭载的嵌入式GPU功耗也高达数十瓦。针对这些挑战，本研究提出了一种嵌入式神经形态测试平台，该平台采用SPiking Low-power Event-based ArchiTecture（SPLEAT）加速器。通过扩展版Qualia框架，我们能够在FPGA实现的SPLEAT平台上完成脉冲神经网络的训练、评估、量化和部署。利用该测试平台，我们加载了前沿的SNN解决方案，评估了在专用硬件上部署网络带来的性能损失，并在专为低功耗脉冲神经网络设计的神经形态硬件上运行了真实场景的事件驱动OD任务。值得注意的是，我们的嵌入式脉冲解决方案（包含108万个参数的模型）能高效运行，单次预测仅需消耗490毫焦能量。