SPDX-License-Identifier: Apache-2.0

Hailo AI Accelerator Integration

Overview

The Hailo-8 and Hailo-8L are dedicated AI inference accelerators designed for edge deployment. They provide high-throughput neural network inference at low power consumption, making them ideal for real-time perception on mobile robot platforms.

By offloading inference from the main CPU/GPU, the Hailo accelerator frees compute resources for navigation, planning, and other workloads.

Supported Hardware

Accelerator	Performance	Power	Form Factor
Hailo-8	26 TOPS	~2.5 W	M.2 Key B+M, mPCIe
Hailo-8L	13 TOPS	~1.5 W	M.2 Key B+M, mPCIe

Both modules connect over PCIe and are compatible with standard carrier boards including Raspberry Pi 5 (via M.2 HAT) and NVIDIA Jetson carriers.

Prerequisites

• Ubuntu 22.04 or later (aarch64 or x86_64)
• HailoRT >= 4.17
• Python >= 3.10
• hailo_platform Python package
• PCIe slot with the Hailo module physically installed

Installation (Ubuntu)

# 1. Add Hailo APT repository
wget -qO - https://hailo.ai/keys/hailo-archive-keyring.gpg | sudo tee /usr/share/keyrings/hailo-archive-keyring.gpg > /dev/null
echo "deb [signed-by=/usr/share/keyrings/hailo-archive-keyring.gpg] https://apt.hailo.ai/ jammy main" | sudo tee /etc/apt/sources.list.d/hailo.list

# 2. Install HailoRT and development libraries
sudo apt update
sudo apt install -y hailort hailort-dev

# 3. Install Python bindings
pip install hailo_platform hailort

# 4. Verify installation
hailortcli fw-control identify

Model Compilation (ONNX to HEF)

Hailo models run in the proprietary HEF (Hailo Executable Format). Use the Dataflow Compiler (DFC) to convert standard models:

# Install the Dataflow Compiler (requires Hailo Developer Zone access)
pip install hailo_dataflow_compiler

# Parse ONNX model
hailo parser onnx yolov8n.onnx

# Optimize and compile to HEF
hailo optimize yolov8n.har
hailo compiler yolov8n.har --hw-arch hailo8
# Output: yolov8n.hef

The resulting .hef file is what you pass to the ROS2 node via the model_path parameter.

ROS2 Node Usage

Launch

ros2 launch robot_perception perception.launch.py \
  model_path:=/opt/models/yolov8n.hef \
  confidence_threshold:=0.6 \
  device_id:=0

Parameters

Parameter	Type	Default	Description
model_path	string	""	Path to compiled HEF model
confidence_threshold	float	0.5	Minimum detection confidence
device_id	int	0	Hailo device index
input_topic	string	/camera/image_raw	Camera image subscription topic
output_topic	string	/perception/detections	Detection results publish topic

Topics

Topic	Type	Direction
/camera/image_raw	sensor_msgs/msg/Image	Subscribe
/perception/detections	vision_msgs/msg/Detection2DArray	Publish

Example: YOLOv8n Object Detection Pipeline

1. Compile yolov8n.onnx to yolov8n.hef using the DFC workflow above
2. Place the HEF at /opt/models/yolov8n.hef
3. Launch camera and perception nodes:

ros2 launch robot_perception perception.launch.py model_path:=/opt/models/yolov8n.hef

1. Visualize detections in RViz2 or consume from downstream planning nodes

Performance Expectations

Model	Accelerator	Latency (ms)	FPS	Power
YOLOv8n	Hailo-8	~8	~120	~2.5 W
YOLOv8n	Hailo-8L	~15	~65	~1.5 W
YOLOv8s	Hailo-8	~14	~70	~2.8 W

Actual performance depends on input resolution, batch size, and system thermal conditions.

Troubleshooting

Issue	Solution
hailortcli reports "No devices found"	Check PCIe connection; run lspci \| grep Hailo; reload driver with sudo modprobe hailo_pci
Import error for hailo_platform	Ensure pip install hailo_platform matches your HailoRT version
Model fails to load	Verify HEF was compiled for the correct hardware arch (hailo8 vs hailo8l)
Low FPS despite accelerator present	Check thermal throttling; ensure PCIe link is Gen3 x1 or better
Node runs in passthrough mode	hailo_platform is not installed or the module is not detected
Permission denied on device	Add user to hailo group: sudo usermod -aG hailo $USER and re-login