Doubling Down: Google’s AIY Endeavor Leads To A Brand New Board

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We are all makers

Doubling Down: Google’s AIY Endeavor Leads To A Brand New Board
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Tune into Public Lab’s Community Science Tool-Building Workshops This Month

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In 2016 I started AIY (do-it-yourself AI) as a new initiative from Google to help students and makers learn about AI through affordable, hands-on kits.
Over the past two years, we released the AIY Voice Kit and AIY Vision Kit, powered by Raspberry Pi boards and Google’s AI tools to inspire the next generation of engineers.
Looking at our consumers, we were excited to find industry professionals using AIY kits to prototype new product ideas with on-device AI to enhance user privacy, power efficiency, and fast performance when processing neural networks on device.
So, this year we decided to invest more with this audience and offer a new platform of reliable hardware components and software tools that allow them to prototype with on-device AI, in a way that easily scales to production.
Our new products include Google’s Edge TPU chip, a purpose-built ASIC that accelerates neural networks running on-device, delivering fast processing without the overhead of passing data up to the cloud.
Coral products currently offer the Edge TPU in two formats, as a fully integrated development board and a pluggable accessory to existing systems.
Coral Edge TPU dev board
The SoM includes the new NXP iMX8M SoC, connected to our Edge TPU over the PCIe bus.
The baseboard includes a variety of connectors to make it easy to bring in sensor data and attach to peripherals, including a 40-pin GPIO header configured in a way that’s consistent with many accessory boards on the market today.
The Edge TPU USB Accelerator
Our USB Accelerator is a pluggable accessory to upgrade existing systems, for example a Raspberry Pi board.
It has the same Edge TPU chip as the single-board computer for USB 2.0/3.0 systems.
We have a Python SDK to let application developers interact with the Edge TPU chip.
To help you get started, we’re releasing a number of sample Python applications and pre-compiled models built using open source architectures that have been tested for the Edge TPU

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