Developers can now get their hands on Google's souped-up answer to the Raspberry Pi: the $150 Coral Dev Board, which features Google's Edge TPU machine-learning accelerator for low-powered devices that sit on the edge of a network.

Google unveiled the tiny Edge TPU ASIC last July as its low-cost chip for bringing machine learning to sensors that can run machine-learning models on the TensorFlow lite framework.

The Edge TPU now features in the Coral-branded $75 USB 'thum bdrive' accelerator and as part of a removable 'system on module' that ships with a developer baseboard.

The Edge TPU Module includes an NXP i.MX 8M system on chip that consists of a quad-core Cortex-A53 and Cortex-M4F, a Vivante GC7000 Lite Graphics graphics processor, 8GB of eMMC storage, and 1GB of LDDR4 RAM. It also features Wi-Fi with 2x2 MIMO (802.11b/g/n/ac 2.4/5GHz) and Bluetooth 4.1. The add-on measures 48mm x 40mm x 5mm.

The baseboard has a RPi-like 40-pin GPIO expansion header, microSD slot for flash memory, USB ports, Gigabit Ethernet port, USB 2.0 and 3.0 ports for power and peripherals, a 3.5mm audio jack, and a terminal to wire up stereo speakers. It measures 88mm x 60mm x 24mm. There's of course video and camera interfaces for computer-vision applications.

Google also launched a $25 five-megapixel Coral Camera Module that connects to the Dev Board via a camera flex cable.

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Google notes that the Dev Board can be used as a single-board computer with accelerated machine-learning processing, but it also serves as an evaluation kit for manufacturers who want to use just the SOM combined with their own custom PCB hardware.

The Edge TPU-powered SOM will be capable of executing "state-of-the-art mobile vision models such as MobileNet v2 at 100+ fps, in a power efficient manner", according to Google.

According to to Hackster.io, Google plans to reveal more about the developer board and USB device at the TensorFlow Dev Summit, which kicks off today.

As Hackster.io notes, the Edge TPU devices would be used in the second stage of developing a machine-learning algorithm after it has already been trained on a large dataset. Developers could then run already trained devices nearer to the data and have devices that use machine learning without the cloud.