AI Accelerator PCIe Card
- Support up to 8~16 x Google® Coral Edge TPU M.2 modules
- Support TensorFlow Lite machine learning framework
- Compatible with PCI Express 3.0 x16 expansion slot
- Optimized thermal design with twin tuborfans
Parallel ML inferences with low latency
Run multiple AI models at the same time.
Enhance ML performance with model-pipelining technology
For applications that require fast response or large-model execution, pipelining techniques enable you to partition models into several smaller models.
- Partition and run : Execute smaller models on different Edge TPUs.
- Rapid response : Increase throughout in high-speed applications.
- Reduce latency : Minimize total latency for large models.
Maximize ML result with small datasets
The Edge TPU is primarily designed for inferencing at the edge. With on-device training, it’s possible to run API-based transfer-learning from a pre-trained model to achieve a fine-tuned model directly on the AI Accelerator PCIe Card.
- Increase model accuracy : Enable transfer learning at the edge via AI Accelerator PCIe card, with no need to for server/cloud interaction for model retraining.
- Save training time : Optimize models using fewer than 200 images, eliminating the need to start from scratch.
![](https://dlcdnwebimgs.asus.com/files/media/52100965-d699-43ee-b93b-277da6a6a84b/img/pic_03.jpg)
![](https://dlcdnwebimgs.asus.com/files/media/52100965-d699-43ee-b93b-277da6a6a84b/img/pic_04.jpg)
Do more with less energy
Designed with energy efficiency in mind, AI Accelerator PCIe Card is equipped with excellent thermal stability to achieve inference acceleration with multiple Edge TPUs.
- Low power consumption : 36/52 W ( 8/16 Edge TPUs).
- No external PSU needed : Power is drawn directly from the PCIe slot.
![](https://dlcdnwebimgs.asus.com/files/media/52100965-d699-43ee-b93b-277da6a6a84b/img/pic_04.jpg)
Prototype AI applications in minutes
If you have a need to build AI demonstrations or prototypes in short order then AI Accelerator PCIe Card is ready to help. We've developed an AI-deployment builder, called Edge TPU inference nodes*, in compliance with Node-RED. This programming tool enables flows to be wired together easily using the Edge TPU nodes, all with a single click – avoiding onerous coding during the prototyping stage.
- Intuitive platform : Browser-based with graphical interface, with no need for coding.
- Easy to use : Drag and drop any ML node, wire it up and it’s ready to deploy.
- Data visualization : Monitor usage metrics of the AI Accelerator PCIe Card via the beautifully-designed dashboard.
* Download Edge TPU inference nodes here
![](https://dlcdnwebimgs.asus.com/files/media/52100965-d699-43ee-b93b-277da6a6a84b/img/pic_05.jpg)
Applications
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Manufacturing
Defect detection
Utilities monitoring
Safety -
Retail
In-store automated checkout
Crowd -density analysis
Intelligent signage -
Transportation
Traffic management
Fleet management
Parking -
Surveillance
Intrusion
Virtual fencing
Security
Workflow
How AI Models are deployed on ASUS AI Accelerator PCIe Card
![](https://dlcdnwebimgs.asus.com/files/media/52100965-d699-43ee-b93b-277da6a6a84b/img/pic_07.png)
- TensorFlow Lite converter : Converts TensorFlow models (with .pb file extension) to TensorFlow Lite models (with .tflite file extension).
- Compiler : A command-line tool that compiles a TensorFlow Lite models (with .tflite file extension) into files that can be run on an Edge TPU.
- Deploy : To execute AI models via PyCoral API (Python) or Libcoral API (C++).
ML model requirement
- ML framework support : TensorFlow Lite.
- Quantization : Tensor parameters are quantized (8-bit fixed-point numbers; int8).
- Neural networks support : Convolutional Neural Networks (CNN).
- Model conversion : TensorFlow model to TensorFlow lite model via TensorFlow converter tool.
Technical specifications
![](https://dlcdnwebimgs.asus.com/files/media/52100965-d699-43ee-b93b-277da6a6a84b/img/pic_08.png)
- ML accelerator : Integrated with 8/16 Edge TPUs to achieve performance 32/64 TOPS
- Interface : PCI Express® (PCIe®) 3.0 x16
- Form factor : Full-height, half-length, double-slot width
- Cooling : Active fan
- Operating temperature : 0-55°C
- Dimensions : 42.1 x 126.3 x 186.3 ( W x H x D mm)
- Power consumption : 36 to 52 watts
- Supported operating systems : Ubuntu 18.04, Debian 10 and Windows 10
Ordering information
- CRL-G18U-P3D : Integrated with 8 Edge TPUs
- CRL-G116U-P3D : Integrated with 16 Edge TPUs