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关键词：
树莓派5 PCIE Switch TPU DTPU 驱动安装 操作演示 AI google Coral
概述：
MPDTPU是一款专为树莓派5设计的双TPU扩展板，通过PCIE Switch芯片将一路PCIE 1x Gen2扩展为两路PCIE信号，从而驱动来自Coral的双TPU模组。树莓派系统下使用TPU模块需要安装驱动以及操作环境配置，本文操作演示基于MPDTPU扩展板，对于单TPU的驱动安装也适用。
注意：此操作演示需要确保能够连接外网，否则许多文件无法下载。
关于双TPU的官方描述：
PRODUCT DETAILS
The Coral M.2 Accelerator with Dual Edge TPU is an M.2 module that brings two Edge TPU coprocessors to existing systems and products with an available M.2 E-key slot.
Features
Performs high-speed ML inferencing: Each Edge TPU coprocessor is capable of performing 4 trillion operations per second (4 TOPS), using 2 watts of power. For example, it can execute state-of-the-art mobile vision models such as MobileNet v2 at almost 400 FPS, in a power-efficient manner. With the two Edge TPUs in this module, you can double the inferences per second (8 TOPS) in several ways, such as by running two models in parallel or pipelining one model across both Edge TPUs.
Works with Debian Linux and Windows: Integrates with Debian-based Linux or Windows 10 systems with a compatible card module slot.
Supports TensorFlow Lite: No need to build models from the ground up. TensorFlow Lite models can be compiled to run on the Edge TPU.
Supports AutoML Vision Edge: Easily build and deploy fast, high-accuracy custom image classification models to your device with AutoML Vision Edge.
Description
The Coral M.2 Accelerator with Dual Edge TPU is an M.2 module (E-key) that includes two Edge TPU ML accelerators, each with their own PCIe Gen2 x1 interface.
The Edge TPU is a small ASIC designed by Google that accelerates TensorFlow Lite models in a power efficient manner: each one is capable of performing 4 trillion operations per second (4 TOPS), using 2 watts of power—that's 2 TOPS per watt. For example, one Edge TPU can execute state-of-the-art mobile vision models such as MobileNet v2 at almost 400 frames per second. This on-device ML processing reduces latency, increases data privacy, and removes the need for a constant internet connection.
With the two Edge TPUs in this module, you can double the inferences per second (8 TOPS) in several ways, such as by running two models in parallel or pipelining one model across both Edge TPUs.
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