Google Coral Dev Board Developer Kit

$ 249.00

Coral Dev Board

Coral Dev Board is a single-board computer with a removable system-on-module (SOM) that contains eMMC, SOC, wireless radios, and Google’s Edge TPU. It’s perfect for IoT devices and other embedded systems that demand fast on-device ML inferencing. The Dev Board can be used as a single-board computer for accelerated ML processing in a small form factor, or as an evaluation kit for the on-board SOM. Additional 40mm × 48mm SOMs can be combined with your custom PCB hardware using board-to-board connectors for integration into products.

The SOM is based on NXP's iMX8M system-on-chip (SOC), but its unique power comes from the Edge TPU coprocessor. The Edge TPU is a small ASIC designed by Google that provides high performance ML inferencing with a low power cost. For example, it can execute state-of-the-art mobile vision models such as MobileNet v2 at 100+ fps in a power efficient manner.

Edge TPU key benefits:
- High speed TensorFlow Lite inferencing
- Low power
- Small footprint



NXP i.MX 8M SOC (Quad-core Cortex-A53, plus Cortex-M4F)
Google Edge TPU ML Accelerator Coprocessor
Cryptographic coprocessor

Wi-Fi 2x2 MIMO (802.11b/g/n/ac 2.4/5GHz)
Bluetooth 4.1


USB Connections

USB Type-C power port (5V DC)
USB 3.0 Type-C OTG port
USB 3.0 Type-A host port
USB 2.0 Micro-B serial console port

Audio Connections

3.5mm audio jack (CTIA compliant)
Digital PDM microphone (x2)
2.54mm 4-pin terminal for stereo speakers

Video Connections

HDMI 2.0a (full size)
39-pin FFC connector for MIPI-DSI display (4-lane)
24-pin FFC connector for MIPI-CSI2 camera (4-lane)

MicroSD card slot
Gigabit Ethernet port
40-pin GPIO expansion header

Supports Debian Linux
Models are built using TensorFlow
Fully supports MobileNet and Inception architectures though custom architectures are possible
Compatible with Google Cloud

Free shipping to usa

Demo Software included:
- Object Recognition
- Image Classification


You will also need:

  • AC power with type C connector
  • 32 or 64 gb microSD card