TUOPUONE Hailo-8 AI M.2 Module User Manual

1. Einleitung

The TUOPUONE Hailo-8 AI M.2 Accelerator Module is designed to provide high-performance artificial intelligence (AI) inferencing capabilities for edge devices, particularly compatible with Raspberry Pi 5. This module integrates the powerful 26 Tera-Operations Per Second (TOPS) Hailo-8 AI Processor, offering an efficient solution for various AI applications.

Hailo-8 AI Kit overview with module and features

Abbildung 1: Überview of the Hailo-8 AI Kit, highlighting the M.2 AI Accelerator Module and its key features such as 26 TOPS, low power consumption, flexibility, and expandability.

Hauptmerkmale:

Hohe Leistung: Powered by a 26 TOPS Hailo-8 AI Processor.
Geringer Stromverbrauch: Typical power consumption of 2.5W.
Skalierbarkeit: Enables simultaneous processing of multiple streams and models for real-time, low-latency AI inferencing.
Broad Framework Support: Compatible with TensorFlow, TensorFlow Lite, ONNX, Keras, and PyTorch.
Betriebssystemkompatibilität: Supports Linux and Windows systems.
Großer Temperaturbereich: Operates reliably from -40°C to 85°C.

2. Spezifikationen

This section details the technical specifications and performance parameters of the Hailo-8 AI M.2 Module.

Figure 2: Detailed parameters for the Hailo-8 AI M.2 Module, including AI performance, form factor, power supply, power consumption, interface, and operating conditions.

Hailo-8 AI M.2 Module Parameters:

Parameter	Wert
KI-Leistung	26 TOPS
Formfaktor	M.2-Taste M
Stromversorgung	3.3 V ±5 %
Energieaufnahme	2.5 W (typisch), 8.65 W (max.)
Schnittstelle	PCIe Gen3, 4-lane
Zertifikate	CE, FCC-Klasse A
Lagertemperatur	-40°C bis 85°C
Betriebstemperatur	-40°C bis 85°C
Luftfeuchtigkeit bei Betrieb	5% - 90% RH (no frosting)
Maße	22×80mm with breakable extensions to 22×42mm and 22×60mm

Table of Hailo-8 performance parameters for various NN models

Figure 3: Performance parameters of the Hailo-8 for different Neural Network (NN) models, including input resolution, mAP, and FPS.

3. Einrichtung und Installation

This section provides instructions for installing the Hailo-8 AI M.2 Module, particularly with the Raspberry Pi 5.

Diagram showing connection of Hailo-8 M.2 module to Raspberry Pi 5 via 16PIN cable

Figure 4: Illustration of the Hailo-8 M.2 module connected to a Raspberry Pi 5, detailing the 16PIN cable connection and power monitoring chip.

Connecting to Raspberry Pi 5:

Ensure your Raspberry Pi 5 is powered off and disconnected from any power source.
Locate the PCIe interface on your Raspberry Pi 5.
Connect the 16PIN cable to the designated port on the Hailo-8 M.2 HAT+ adapter. Ensure the triangles on the cable and connector align correctly to prevent damage.
Connect the other end of the 16PIN cable to the Raspberry Pi 5's PCIe interface.
Carefully insert the Hailo-8 AI M.2 Module into the M.2 slot on the HAT+ adapter. Secure it with the provided screw.
Mount the HAT+ adapter onto the Raspberry Pi 5's GPIO pins.
If using an optional cooling fan, install it according to its instructions, ensuring proper airflow for the AI module.

Explodierte view diagram showing how to install the Hailo-8 AI M.2 module onto a Raspberry Pi 5

Abbildung 5: Explosionsdarstellung view illustrating the assembly process of the Hailo-8 AI M.2 module with a Raspberry Pi 5 and an optional cooling solution. Note: The cooling fan is not included.

4. Bedienungsanleitung

The Hailo-8 AI M.2 Module is designed for seamless integration into various AI development environments.

Software and Frameworks:

The module supports popular AI frameworks including TensorFlow, TensorFlow Lite, ONNX, Keras, and PyTorch.
It is compatible with both Linux and Windows operating systems, allowing for flexible development and deployment.
Utilize Hailo's comprehensive Dataflow Compiler and software toolset to port Neural Network models efficiently to the Hailo-8.

Power Monitoring and Cooling:

The onboard power monitoring chip and EEPROM provide real-time device power status, contributing to stable operation.
For optimal performance and longevity, especially under heavy AI workloads, consider utilizing the reserved airflow vent for a cooling fan. This helps dissipate heat and maintain module performance.

5. Wartung

To ensure the longevity and optimal performance of your Hailo-8 AI M.2 Module, follow these general maintenance guidelines:

Umgebungsbedingungen: Operate the module within the specified temperature range of -40°C to 85°C and humidity range of 5% - 90% RH (non-condensing).
Sauberkeit: Keep the module free from dust and debris. Use compressed air or a soft brush for cleaning if necessary. Avoid using liquids or harsh chemicals.
Physische Handhabung: Handle the module by its edges to avoid touching sensitive components. Static electricity can damage electronic components, so use anti-static precautions when handling.
Firmware-Updates: Regularly check the TUOPUONE or Hailo Technologies website for any available firmware or software updates to ensure the best performance and compatibility.

6. Fehlerbehebung

If you encounter issues with your Hailo-8 AI M.2 Module, consider the following troubleshooting steps:

Kein Nachweis: Ensure the module is correctly seated in the M.2 slot and the 16PIN cable is securely connected to both the HAT+ adapter and the Raspberry Pi 5. Verify the cable orientation is correct (aligning triangles).
Probleme mit der Stromversorgung: Confirm that your Raspberry Pi 5 has an adequate power supply. The Hailo-8 module requires 3.3V ±5%.
Software-Kompatibilität: Verify that your operating system (Linux/Windows) and AI frameworks (TensorFlow, etc.) are correctly installed and configured according to Hailo's documentation.
Überhitzung: If the module experiences performance degradation or unexpected shutdowns, check for proper cooling. Ensure any optional cooling fans are functioning and not obstructed.
Leistungsprobleme: Ensure your AI models are optimized for the Hailo-8 processor using the provided Dataflow Compiler. Check for any resource conflicts with other components.

For persistent issues, refer to the official documentation from Hailo Technologies or contact TUOPUONE support.

7. Anwendungen

The Hailo-8 AI M.2 Module is suitable for a wide range of edge AI applications due to its high performance and low power consumption.

Examples of AI applications: Generative AI, ITS/Perimeter Security, Industrial Automation, Smart Retail

Abbildung 6: Bspamples of diverse applications where the Hailo-8 AI M.2 Module can be deployed, including generative AI on PCs, intelligent transportation systems, industrial automation, and smart retail solutions.

Typical Use Cases:

Generative AI on PC: Accelerating generative AI workloads directly on personal computers.
ITS/Perimeter Security/Access Control: Enabling real-time alerts and decision-making for security systems.
Industrielle Automatisierung: Powering automatic optical detection and other AI-driven processes in industrial settings.
Smart Retail: Enhancing shopping experiences through AI-driven analytics and automation.

8. Abmessungen

The physical dimensions of the Hailo-8 AI M.2 Module are provided below.

Outline dimensions of the Hailo-8 AI M.2 module in millimeters

Figure 7: Outline dimensions of the Hailo-8 AI M.2 Module, showing a width of 22mm and a length of 80mm, with breakable sections for 42mm and 60mm lengths. All units are in millimeters.

9. Garantie und Support

For product support, technical assistance, or warranty inquiries, please contact TUOPUONE directly. Information regarding protection plans may also be available at the point of purchase.

Always refer to the official TUOPUONE website or product page for the most up-to-date support information and resources.