Google Coral USB Accelerator – Edge TPU Machine Learning Coprocessor

Bring high-speed, local AI inferencing to your existing systems.

Unlock the power of offline artificial intelligence with the Google Coral USB Accelerator. This compact USB accessory features the Google Edge TPU (Tensor Processing Unit), a purpose-built ASIC designed to accelerate TensorFlow Lite models. Whether you are a developer, maker, or IoT engineer, this device allows you to run modern computer vision and ML models locally—fast, privately, and efficiently.

Why Choose the Google Coral USB Accelerator?

• Supercharged Inference Speed: Capable of performing 4 trillion operations per second (4 TOPS), this accelerator runs state-of-the-art mobile vision models (like MobileNet v2) at nearly 400 FPS. It is significantly faster than running models on a standard CPU or Raspberry Pi alone.

• Low Power, High Efficiency: Designed for edge computing, the onboard Edge TPU delivers incredible performance using just 2 watts of power. It connects via USB 3.0 Type-C for high-speed data transfer and power delivery.

• Complete Privacy & Offline Capability: Process data locally on your device. By removing the need to send data to the cloud, you reduce latency, eliminate server costs, and ensure user privacy—perfect for secure video analytics and smart home applications.

• Seamless Compatibility: Works instantly with Raspberry Pi (3 Model B+, 4, 5), Linux (Debian/Ubuntu), macOS, and Windows 10. It’s the easiest way to add AI capabilities to your existing hardware.
• TensorFlow Lite Native: Built to run tflite models natively. Easily compile your custom models or use Google’s pre-trained library for object detection, pose estimation, keyphrase detection, and image segmentation.

Technical Specifications

• ML Accelerator: Google Edge TPU coprocessor (ASIC)

• Peak Performance: 4 TOPS (int8)

• Power Efficiency: 2 TOPS per watt

• Interface: USB 3.0 Type-C (SuperSpeed 5Gb/s)

• Power Consumption: 5V via USB (approx. 900mA peak current)

• Dimensions: 65 mm x 30 mm x 8 mm

• Weight: ~20g (device only)

• Supported OS: Linux (Debian 10+), macOS (10.15+ via MacPorts/Homebrew), Windows 10

• Supported Architectures: x86-64, ARMv7 (32-bit), ARMv8 (64-bit)

• Framework Support: TensorFlow Lite