Coral M.2 Accelerator with A+E Key | Edge TPU Machine Learning Coprocessor | 4 TOPS AI Module
Description
The (SPIDER Automation) Coral M.2 Accelerator is an advanced M.2 module designed to integrate Google’s Edge TPU machine learning coprocessor into existing industrial systems, embedded platforms, and mini-PCs. Powered by a custom-built ASIC, the Edge TPU accelerates TensorFlow Lite models in an exceptionally power-efficient manner, delivering up to 4 trillion operations per second (4 TOPS) while consuming only 2 watts of power.
By performing machine learning inferencing directly on the device, this module dramatically reduces latency, enhances data privacy, and eliminates the necessity for a constant cloud connection. Compatible with systems featuring an M.2 A+E key card slot, it provides a scalable solution for high-performance computer vision tasks—such as running MobileNet v2 models at hundreds of frames per second.
Key Features
- Google Edge TPU Core: Dedicated custom ASIC optimized for efficient TensorFlow Lite model execution.
- High Compute Performance: Delivers 4 TOPS total peak performance (int8) at an energy efficiency of 2 TOPS per watt.
- M.2 A+E Key Interface: Utilizes the standard M.2-2230-A-E-S3 form factor for easy physical integration.
- PCIe Gen2 x1 Bus: High-bandwidth data transfer interface ensuring fast host-to-accelerator communication.
- Integrated Power Management: Built-in power regulation ensures stable operation across varying thermal loads.
- Wide Operating Range: Engineered to function reliably in environments spanning from -20°C to +85°C.
Benefits
-
- Ultra-Low Power AI Processing: Run complex neural networks locally on edge hardware with minimal energy consumption.
- Reduced Network Latency: Eliminate round-trip cloud delays by executing computer vision and classification tasks locally.
- Enhanced Data Privacy: Keep sensitive video and telemetry data secure on your local hardware device.
- Offline Operation: Execute real-time machine learning workflows entirely independent of internet connectivity.
- Scalable Architecture: Pipeline models across multiple Edge TPUs or deploy multiple modules for heavier workloads.
- Standardized Expansion: Compact M.2 footprint fits effortlessly into industrial gateways and custom mini-PC builds.
Applications
- Industrial Automation & Vision Systems: Real-time defect detection, sorting, and quality assurance on automated assembly lines.
- Smart Surveillance Gateways: Edge-based object detection, face recognition, and anomaly alerts in security hardware.
- Robotics & Autonomous Systems: Localized environmental parsing and navigation decision-making for robotic platforms.
- Edge AI Prototyping: Developing localized neural network models on mini-PCs and custom embedded Linux boards.
- IoT Smart Environments: Automated environmental sensing and response triggers using lightweight vision models.
- Medical & Scientific Instruments: Embedded pattern recognition and local data classification without cloud reliance.
Package Includes
- 1 x Coral M.2 Accelerator Module
Specifications
| Parameter | Specification |
|---|---|
| Item Type | Machine Learning Accelerator Module |
| Brand | SPIDER Automation |
| Model Series | Coral |
| Product Name | Coral M.2 Accelerator |
| Processor / ASIC | Google Edge TPU ML Accelerator |
| Performance | 4 TOPS (int8) peak performance at 2 TOPS/Watt |
| Hardware Interface | M.2 A+E Key (M.2-2230-A-E-S3) |
| Bus Interface | PCIe Gen2 x1 |
| Operating Temperature | -20°C to +85°C |
| Gross Shipping Weight | 0.015 kg |
| Package Shipping Dimensions | 6 cm × 5 cm × 2 cm |
