Google Coral Dev Board

schematics

published (baseboard schematic + Altium source + Allegro layout, Apache-2.0

toolchain

partial (open runtime (libedgetpu) + TFLite, closed Edge TPU compiler)

datasheets

public

blobs

required (proprietary Edge TPU ASIC + closed compiler)

retail

open_market (broadly purchasable)

form_factor

board (baseboard carrying a Google SoM, sold as one board)

Google publishes the baseboard's schematic and its Altium and Allegro design files under Apache-2.0, alongside a public datasheet and an open-source runtime driver, libedgetpu. But the system-on-module that carries the Edge TPU chip has no published design files, and the Edge TPU Compiler that every model must go through is distributed only as a binary, with no source published.

• https://coral.ai/docs/dev-board/datasheet/ recorded 2026-08-14

  Datasheet v1.7 (December 2022) - 4 TOPS, 2 TOPS/W, SoM form factor, TFLite, Google-designed Edge TPU; its "Schematic and layout files" section links Coral-Dev-Board-baseboard-schematic.pdf, Coral-Dev-Board-baseboard-schematic-Altium.zip, Coral-Dev-Board-baseboard-layout-Allegro.brd and Dev Board.STEP, and points anyone wanting the SoM on custom hardware at the SoM datasheet rather than at design files

• https://coral.ai/docs/edgetpu/compiler/ recorded 2026-08-14

  The Edge TPU Compiler is distributed as an apt package from packages.cloud.google.com and nothing else - installation is an apt-key add plus apt-get install, the page publishes a version history to 16.0 and points at no source repository, no build instructions and no license. It "compiles a TensorFlow Lite model (.tflite file)" and nothing else. The closed half of the toolchain, and the blob that stands between any model and the Edge TPU

• https://coral.ai/products/dev-board recorded 2026-08-14

  the product page for this board 302-redirects to developers.google.com/coral, which names no hardware and links no purchase route - establishes that the vendor no longer offers the board for sale

• https://github.com/google-coral/electricals recorded 2026-08-14

  "Electrical designs for coral.ai projects", Apache-2.0, public; dev_board/ holds Coral-Dev-Board-baseboard-schematic.pdf, -schematic-Altium.zip and -layout-Allegro.brd, which is where the datasheet download links resolve to

• https://github.com/google-coral/libedgetpu recorded 2026-08-14

  "The userspace level runtime driver for Coral devices", Apache-2.0, source published - the open half of the toolchain. The repo carries "This repository was archived by the owner on Oct 14, 2025. It is now read-only", last pushed 2024-08-11. The source is still published under the same licence, so the openness value is unaffected; what the archive bears on is adoption

• https://www.seeedstudio.com/Coral-4GB-Dev-Board-Version-p-4683.html recorded 2026-08-14

  Coral Dev Board 4GB, SKU 102110458, $169.99, marked Discontinued and Out of stock at the board's named reseller. The 1GB listing at Coral-Dev-Board-p-2900 reads the same way, $129.99, Discontinued and out of stock. Read for `retail` and it does not support `open_market`, which is why the openness axis above is left undated

Google archived the Edge TPU software stack on 2025-10-14, and its Coral product page no longer names any board. Boards already in the field keep running the archived stack, which is why adoption has not fallen further even as the platform winds down.

• https://api.github.com/orgs/google-coral/repos?per_page=100 recorded 2026-08-14

  34 of 40 repositories archived, including the whole Edge TPU stack (libedgetpu archived 2025-10-14, libcoral, pycoral, tflite, examples-camera, tutorials), all last pushed 2024-08-11; the six live repos are coralnpu/coralnpu-compiler/coralnpu-mpact/coralmicro plus two demos, a different product line

The Edge TPU delivers 4 TOPS at 2 TOPS per watt, limited to int8-quantized TensorFlow Lite models and suited to small CNN inference rather than large or generative models. That keeps it below the 6 TOPS RK3588 boards.

• https://coral.ai/docs/dev-board/datasheet/ recorded 2026-08-13

  "High-speed and low-power ML inferencing (4 TOPS @ 2 W)", "4 trillion operations per second (TOPS)", "2 TOPS per watt", and an Edge TPU coprocessor used "with TensorFlow Lite models"