產品說明6

Raspberry Pi AI Kit 人工智慧套件，原廠PCIe M.2、Hailo AI擴充套件、適用於Pi5B

Raspberry Pi AI Kit with M.2 HAT and Hailo AI Module 

　　Please Be Aware: This is a M.2 HAT+ for devices that use the M.2 specification. The SparkFun MicroMod line uses a different pinout despite fitting in the same connector as M.2 devices. The MicroMod line does not work with this HAT.／請注意：這是適用於使用 M.2 規範的設備的 M.2 HAT+。儘管與 M.2 設備安裝在相同的連接器中，SparkFun MicroMod 系列仍使用不同的引腳排列。 MicroMod 系列不適用於此 HAT。

［INCLUDES］清單：

• Hailo AI module containing a Neural Processing Unit (NPU)
• Raspberry Pi M.2 HAT+, to connect the AI module to your Raspberry Pi 5
  
• Thermal pad pre-fitted between the module and the M.2 HAT+
  
• Mounting hardware kit
  
• 16mm stacking GPIO header

　　The Raspberry Pi AI Kit bundles an M.2-format Hailo 8L AI accelerator with the Raspberry Pi M.2 HAT+ to offer you access to excellent inferencing performance on the Raspberry Pi 5. The AI module is a 13 tera-operations per second (TOPS) neural network inference accelerator built around the Hailo-8L chip. The module uses the M.2 2242 form factor, and comes pre-installed in the M.2 HAT+, to which it connects through an M key edge connector. The M.2 HAT+ communicates between the AI module’s M.2 interface and the Raspberry Pi 5’s PCIe 2.0 interface.

　　When the host Raspberry Pi 5 is running an up-to-date Raspberry Pi OS image, it automatically detects the Hailo module and makes the NPU available for AI computing tasks. The built-in rpicam-apps camera applications in Raspberry Pi OS natively support the AI module, automatically using the NPU to run compatible post-processing tasks.

 ［FEATURES］特徵：

• Hailo 8L accelerator with up to 13 TOPS (Trillions of Operations Per Second) inferencing performance.
• Conforms to the HAT+ specification.
• Supplied pre-assembled on a Raspberry Pi M.2 HAT+ so ready out of the box to use with a Raspberry Pi 5.
• Fully integrated into Raspberry Pi’s camera software stack.
• Supplied with a 16mm stacking header and spacers/screws to enable the M.2 HAT+ to be fitted to a Raspberry Pi 5 with the Active Cooler in place.

 

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