0006 MPTPU(M.2 E)

关键词

树莓派5、PCIE、TPU、DTPU、驱动安装、操作演示、AI、google、Coral

一、简介

MPW7/TPU是一款专为树莓派5设计的TPU扩展板，通过PCIE 1x Gen2驱动来自Coral的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MPW7/TPU扩展板，对于双TPU的驱动安装也适用。

注意：此操作演示需要确保能连通外网（需自备方法），否则许多文件无法下载。

二、硬件资源

关于双TPU的官方描述（本文档所演示的单TPU也可参考此描述）：

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.

三、系统烧写

3.1 本文档使用的镜像为2024-07-04-raspios-bookworm-arm64-full.img.xz（树莓派OS，Raspberry Pi OS with desktop and recommended software）。

树莓派OS下载地址：

https://www.raspberrypi.com/software/operating-systems/#raspberry-pi-os-64-bit

3.2 系统烧写在SD（TF）卡上，点击直达烧写方法说明。

四、驱动安装与配置

4.1