标题： HOMI：事件相机的超快速边缘AI平台 显示英文标题

标题： HOMI: Ultra-Fast EdgeAI platform for Event Cameras

摘要： 事件相机由于其异步操作和稀疏的事件驱动输出，为边缘机器人应用提供了显著优势，使其非常适合需要快速和高效闭环控制的任务，例如基于手势的人机交互。 尽管具有这种潜力，现有的事件处理解决方案仍然有限，通常缺乏完整的端到端实现，表现出高延迟，并且未能充分利用事件数据的稀疏性。 在本文中，我们提出了HOMI，这是一个超低延迟的端到端边缘AI平台，包括一个Prophesee IMX636事件传感器芯片和一个Xilinx Zynq UltraScale+MPSoC FPGA芯片，部署了一个自主研发的AI加速器。 我们开发了硬件优化的预处理管道，支持直方图累积的恒定时间和恒定事件模式，以及线性和指数时间表面。 我们的通用实现适用于以准确性为导向和低延迟的应用。 当配置为高精度操作时，HOMI在DVS Gesture数据集上实现了94%的准确率，并且在低延迟配置下提供了1000 fps的吞吐量。 硬件优化的管道保持了紧凑的内存占用，并且仅使用了FPGA上可用LUT资源的33%，为进一步降低延迟、模型并行化、多任务部署或集成更复杂架构留下了充足的空间。 显示英文摘要

摘要： Event cameras offer significant advantages for edge robotics applications due to their asynchronous operation and sparse, event-driven output, making them well-suited for tasks requiring fast and efficient closed-loop control, such as gesture-based human-robot interaction. Despite this potential, existing event processing solutions remain limited, often lacking complete end-to-end implementations, exhibiting high latency, and insufficiently exploiting event data sparsity. In this paper, we present HOMI, an ultra-low latency, end-to-end edge AI platform comprising a Prophesee IMX636 event sensor chip with an Xilinx Zynq UltraScale+MPSoC FPGA chip, deploying an in-house developed AI accelerator. We have developed hardware-optimized pre-processing pipelines supporting both constant-time and constant-event modes for histogram accumulation, linear and exponential time surfaces. Our general-purpose implementation caters to both accuracy-driven and low-latency applications. HOMI achieves 94% accuracy on the DVS Gesture dataset as a use case when configured for high accuracy operation and provides a throughput of 1000 fps for low-latency configuration. The hardware-optimised pipeline maintains a compact memory footprint and utilises only 33% of the available LUT resources on the FPGA, leaving ample headroom for further latency reduction, model parallelisation, multi-task deployments, or integration of more complex architectures.