HOMI: Ultra-Fast EdgeAI platform for Event Cameras

TL;DR

HOMI is an ultra-fast, end-to-end EdgeAI platform for event cameras that achieves high accuracy and low latency, enabling efficient real-time applications in robotics and gesture recognition.

Contribution

The paper introduces HOMI, a hardware-optimized, low-latency EdgeAI platform with an end-to-end pipeline and a custom AI accelerator for event camera data processing.

Findings

Achieves 94% accuracy on DVS Gesture dataset.

Provides 1000 fps throughput for low-latency applications.

Uses only 33% of FPGA LUT resources, allowing further scalability.

Abstract

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.