  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.
