Bio-Inspired Event Cameras for Robust Edge System in Challenging Environments

Deploying real-time intelligent systems at the edge poses significant challenges in dynamic, resource-constrained, and low-visibility environments. We present a bio-inspired event camera architecture for robust and adaptable continuous target detection and tracking under tough environmental conditions. Leveraging the high temporal resolution and dynamic range of event-based sensing, our approach addresses key limitations in small object detection and low-light imaging. Built on HARP (Hierarchical Attention-Oriented Region-Based Processing [16]), our hardware platform prioritizes salient regions and performs early-stage, sensor-level information extraction. This attentionguided strategy reduces redundant spatiotemporal processing and enables learning models to focus computation on critical regions. A hierarchical and parallel pipeline further maximizes throughput by exploiting high-bandwidth image access. We prototyped the system on FPGA and validated its performance in two key tasks: small object detection and low-light enhancement. Compared to prior FPGA-based designs, our system reduces logic resource usage by up to 88%, increases throughput by 47%, and lowers latency by over 30%. For enhancement, it achieves 14% lower MSE, 4.5% higher PSNR, and 7.1% higher SSIM. Our quantized detector maintains strong semantic accuracy with only 7.05% degradation from its float baseline, using just 1.1M MACs and 42K parameters-suitable for real-time deployment on embedded platforms.