# HOMI: Ultra-Fast EdgeAI platform for Event Cameras

arXiv: https://arxiv.org/abs/2508.12637 · HTML: https://mlsanity.com/paper/2508.12637/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 13 of 13)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification](https://mlsanity.com/paper/1803.07913/) (2018)
- [NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps](https://mlsanity.com/paper/1706.01406/) (2017)
- [EvGNN: An Event-driven Graph Neural Network Accelerator for Edge Vision](https://mlsanity.com/paper/2404.19489/) (2024)
- [RAMAN: A Re-configurable and Sparse tinyML Accelerator for Inference on Edge](https://mlsanity.com/paper/2306.06493/) (2023)
- [A Composable Dynamic Sparse Dataflow Architecture for Efficient Event-based Vision Processing on FPGA](https://mlsanity.com/paper/2401.05626/) (2024)
- [Event-based vision on FPGAs -- a survey](https://mlsanity.com/paper/2407.08356/) (2024)
- [Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application](https://mlsanity.com/paper/2503.22943/) (2025)
- [Dynamic Vision Sensor integration on FPGA-based CNN accelerators for high-speed visual classification](https://mlsanity.com/paper/1905.07419/) (2019)
- [ColibriUAV: An Ultra-Fast, Energy-Efficient Neuromorphic Edge Processing UAV-Platform with Event-Based and Frame-Based Cameras](https://mlsanity.com/paper/2305.18371/) (2023)
- [Ev-Edge: Efficient Execution of Event-based Vision Algorithms on Commodity Edge Platforms](https://mlsanity.com/paper/2403.15717/) (2024)
- [Towards Low-Latency Event-based Obstacle Avoidance on a FPGA-Drone](https://mlsanity.com/paper/2504.10400/) (2025)
- [High-definition event frame generation using SoC FPGA devices](https://mlsanity.com/paper/2307.14177/) (2023)

## Abstract (arXiv)

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.