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MCDODO
MCDODO Coral M.2 Accelerator B+M Key development board
Performs 4 trillion operations per second at .5 watts per TOPS, running MobileNet v2 at 400 FPS on Debian Linux with TensorFlow Lite and AutoML Vision Edge support
- TypeSingle Board Computers
- Edge TPU delivers 4 trillion ops/sec at .5 watts per TOPS
- Runs MobileNet v2 at 400 FPS for real-time vision
- Works with any Debian Linux system with M.2 slot
- TensorFlow Lite models compile directly for Edge TPU
- AutoML Vision Edge trains and deploys custom image models
Coral M.2 Accelerator B+M Key
The Coral M.2 Accelerator B+M Key is an IoT edge device designed for developers adding on-device machine-learning inference to Debian-based Linux systems. It targets makers and engineers who need a compact accelerator that fits a compatible M.2 slot without requiring a full single-board computer redesign.
4 TOPS Edge TPU capacity
The on-board Edge TPU coprocessor delivers 4 trillion operations per second and runs MobileNet v2 at 400 FPS, giving ample headroom for real-time vision tasks such as object detection or classification. Workloads that demand heavier models or sustained batch processing will exceed the available compute budget and require a higher-performance accelerator.
Efficient thermal profile
At .5 watts per TOPS the module draws very little power, so passive cooling in a typical enclosure is usually sufficient and audible fan noise is not a concern. Continuous heavy inference will raise the board temperature, but the low power envelope keeps thermal throttling rare in normal ambient conditions.
Öne çıkanlar
- Edge TPU delivers 4 trillion ops/sec at .5 watts per TOPS
- Runs MobileNet v2 at 400 FPS for real-time vision
- Works with any Debian Linux system with M.2 slot
- TensorFlow Lite models compile directly for Edge TPU
- AutoML Vision Edge trains and deploys custom image models
Teknik özellikler
| Type | Single Board Computers |
|---|
Bu ürünle ilgili sorular
Will the Coral M.2 Accelerator B+M Key fit in the M.2 slot on my older Debian Linux board?
It integrates with any Debian-based Linux system that has a compatible card module slot, so an older board with the correct M.2 interface will work.
How can I verify the Edge TPU is performing high-speed ML inferencing after installation?
You can confirm it is working by running a TensorFlow Lite model such as MobileNet v2 and observing inference speeds up to 400 FPS.
Does the box include any cables or a heatsink, or do I need to buy them separately?
The board is supplied as a standalone M.2 accelerator module; any mounting hardware, cooling or adapter cables must be sourced separately.
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