MOD5272-100IR

The MOD5272-100IR by NetBurner Inc. introduces groundbreaking innovation in the Embedded - Microcontroller, Microprocessor, FPGA Modules field, specifically tailored for AI-powered vision systems. Its neural processing capabilities combined with ultra-low latency make it perfect for real-time object recognition and machine vision applications in smart cities.Utilizing a MCU, Ethernet Core architecture, this solution delivers unprecedented parallel processing density. The heterogeneous computing approach enables simultaneous execution of multiple neural networks while maintaining precise power sequencing for optimal thermal management.The ColdFire 5272 features dedicated tensor cores for accelerated AI workloads. Its multi-threaded architecture ensures deterministic performance in video analytics pipelines, crucial for surveillance systems requiring frame-by-frame accuracy.Operating at 62MHz, the module achieves industry-leading inference speeds while maintaining power efficiency. The adaptive clock scaling technology dynamically adjusts frequency based on computational load in edge AI applications.With 2MB of high-endurance flash memory, the device supports multiple AI model storage. The wear-leveling algorithm extends memory lifespan in 24/7 operational security systems.The 8.004MB high-bandwidth memory enables real-time processing of 4K video streams. Its low-latency architecture is optimized for frame buffer management in multi-camera setups.The RJ-45, 2x50 Header interface provides robust connectivity for high-speed camera links. Its impedance-controlled design maintains signal integrity for 10Gbps data transmission in medical imaging systems.Measuring 2.6" x 2" (66.04mm x 50.8mm), this compact solution fits space-constrained embedded vision applications. The advanced packaging technology ensures reliable operation in vibration-prone robotics environments.Rated for -40°C ~ 85°C, the module performs reliably in outdoor smart traffic systems. The extended temperature validation includes 1000+ thermal cycles for automotive-grade reliability.