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Google Coral DEV Board Mini Google Coral Dev Board Mini 2 GB 4 x 1.5 GHz
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Highlights & Details
Google Coral DEV Board
- MediaTek 8167 SoC
- Quad-Core ARM Cortex-A35, up to 1.5 GHz, 64-bit ARMv8-A.
- Imagination PowerVR GE8300 GPU (supports volcano 1.0)
- 1080p/60fps HEVC & MPEG4 decoder
- 2 GB LPDDR3 RAM
- 8 GB eMMC Flash memory (pre-installed with Mendel Linux)
Technical specifications
- Wi-Fi 5 (802.11a/b/g/n/ac, 2.4/5GHz) (MediaTek MT7658)
- Bluetooth 5.0 & Bluetooth Low Energy (BLE)
- MicroHDMI (v1.4), supports screens with up to 1920x1080 resolution
- 1 x USB type-C port (USB 2.0) for data, can work as host and as device (OTG)
- 1 x USB type-C port for power supply
- 3.5 mm audio socket
- Digital PDM microphone
- 2.54 mm 2-pin mono loudspeaker terminal
- 24-pin foil cable (FFC) plug for MIPI-CSI2 camera (4 lanes), supports cameras with up to 8 MP resolution
- 24-pin foil cable (FFC) connector for MIPI-DSI display (4 lanes)
- MicroSD slot (supports max. 32GB SD cards)
- 40-pin GPIO header (3.3 V logic level)
- Type
Google Coral Dev Board Mini
- Processor cores (No.)
4 x
2 GB
- Clock speed
1.5 GHz
- External ports
HDMI™
USB-C® (current)
3.5 mm jack
micro SD
- Wi-Fi
b/g/n/ac
- Bluetooth
5.0
- Length
64 mm
- Width
48 mm
- Product type
SBC
Documents & Downloads
Datasheet
Datasheet 2435948 Google Coral DEV Board Mini Google Coral Dev Board Mini 2 GB 4 x 1.5 GHz
Description
The Google Coral Dev Board Mini is a low-cost single-board computer (SBC) with built-in real-time inference module and an EdgeTPU (Tensor Processing Unit) for powerful machine learning / deep learning algorithms.
The developer board integrates a MediaTek 8167 SoC with the Edge TPU. In contrast to the Coral USB Accelerator, it thus represents a stand-alone platform on which you can run your application completely.
With the Edge TPU, Tensor Flow Lite models can be used quickly and energy-saving for inference. A particular advantage of this solution is that your data remains local. This helps with latency and, of course, data protection in accordance with the basic data protection regulation (GDPR).
Google is increasingly using artificial intelligence (AI) and machine learning (ML) to realize its services. To do this, it developed specialized processors called TPU ("tensor processing Unit") for its data centers, which can execute the algorithms faster and more energy-saving with the TensorFlow Framework. For example, Google Maps is enhanced by street-view street signs that are analyzed using a TensorFlow-based neural network. The clou: TensorFlow can be easily programd in Python.
The Edge TPU supports the TensorFlow Lite framework and can perform up to 4 trillion operations per second with only 2 W consumption. TensorFlow Lite is a modified version of TensorFlow that has been specially adapted to the needs of mobile devices and embedded devices. Many tensorFlow applications can also be implemented in TensorFlow Lite.
This text is machine translated.
The developer board integrates a MediaTek 8167 SoC with the Edge TPU. In contrast to the Coral USB Accelerator, it thus represents a stand-alone platform on which you can run your application completely.
With the Edge TPU, Tensor Flow Lite models can be used quickly and energy-saving for inference. A particular advantage of this solution is that your data remains local. This helps with latency and, of course, data protection in accordance with the basic data protection regulation (GDPR).
Google is increasingly using artificial intelligence (AI) and machine learning (ML) to realize its services. To do this, it developed specialized processors called TPU ("tensor processing Unit") for its data centers, which can execute the algorithms faster and more energy-saving with the TensorFlow Framework. For example, Google Maps is enhanced by street-view street signs that are analyzed using a TensorFlow-based neural network. The clou: TensorFlow can be easily programd in Python.
The Edge TPU supports the TensorFlow Lite framework and can perform up to 4 trillion operations per second with only 2 W consumption. TensorFlow Lite is a modified version of TensorFlow that has been specially adapted to the needs of mobile devices and embedded devices. Many tensorFlow applications can also be implemented in TensorFlow Lite.
This text is machine translated.