HOMI: Ultra-Fast EdgeAI platform for Event Cameras
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912541415833600 |
|---|---|
| author | H, Shankaranarayanan Yadav, Satyapreet Singh Krishna, Adithya P, Ajay Vikram Mehendale, Mahesh Thakur, Chetan Singh |
| author_facet | H, Shankaranarayanan Yadav, Satyapreet Singh Krishna, Adithya P, Ajay Vikram Mehendale, Mahesh Thakur, Chetan Singh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_12637 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HOMI: Ultra-Fast EdgeAI platform for Event Cameras H, Shankaranarayanan Yadav, Satyapreet Singh Krishna, Adithya P, Ajay Vikram Mehendale, Mahesh Thakur, Chetan Singh Hardware Architecture Computer Vision and Pattern Recognition Emerging Technologies Neural and Evolutionary Computing 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. |
| title | HOMI: Ultra-Fast EdgeAI platform for Event Cameras |
| topic | Hardware Architecture Computer Vision and Pattern Recognition Emerging Technologies Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2508.12637 |