HOMI: Ultra-Fast EdgeAI platform for Event Cameras
Shankaranarayanan H, Satyapreet Singh Yadav, Adithya Krishna, Ajay Vikram P, Mahesh Mehendale, Chetan Singh Thakur

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…
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