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Raspberry Pi Ai Hat+ 13 Tops SC1785 - RPI5 HAILO AI HAT+ 13T | NEW inbox For Sale
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- The Raspberry Pi AI HAT+ 13 TOPS (Part Number: SC1785) is an add-on board designed for the Raspberry Pi 5, integrating a Hailo-8L AI accelerator to deliver efficient, high-performance neural network inferencing. Released in October 2024, this HAT+ provides an accessible and cost-effective solution for deploying AI applications in embedded systems. With 13 tera-operations per second (TOPS) of processing power, it supports real-time AI workloads such as object detection, semantic segmentation, pose estimation, and more, making it ideal for robotics, industrial automation, security systems, and smart home applications.
Key Features - Hailo-8L AI accelerator delivering 13 TOPS inferencing performance
- Seamless integration with Raspberry Pi 5 via PCIe Gen 3 interface
- Native support in Raspberry Pi OS, including automatic detection and compatibility with rpicam-apps
- Conforms to Raspberry Pi HAT+ specification for plug-and-play functionality
- Includes 16mm stacking header, spacers, and screws for secure mounting
- Operating temperature range: 0°C to 50°C (ambient)
- Guaranteed production lifetime until at least January 2030
- Technical Specifications
- Inference Performance: 13 TOPS (Hailo-8L accelerator)
- Interface: PCIe Gen 3 (direct connection to Raspberry Pi 5)
- Camera Support: Fully integrated with Raspberry Pi’s camera software stack
- Dimensions: 65mm x 56.5mm (compatible with standard HAT+ form factor)
- Power Requirements: Powered via Raspberry Pi 5 (no external PSU Raspberry Pi AI HAT+ 13 TOPS is engineered for developers and engineers working on AI-driven projects. Its compact design and high efficiency make it suitable for:
- Industrial Automation: Real-time quality control and predictive maintenance
- Security & Surveillance: AI-powered object recognition and anomaly detection
- Robotics: Autonomous navigation and human-robot interaction
- Smart Home: Voice and gesture-based automation
- Edge AI: Low-latency inferencing without cloud dependency
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