To enable developers building artificial intelligence AI-driven sensor processing systems, Microchip Technology has released its PolarFire FPGA ethernet sensor bridge that works with the NVIDIA Holoscan sensor processing platform.
PolarFire FPGAs enable multi-protocol support and this first solution to be released as part of Microchips platform is compatible with MIPI CSI-2-based sensors and the MIPI D-PHY physical layer. Future solutions will support a wide range of sensors with different interfaces including SLVS-EC 2.0, 12G SDI, CoaXPress 2.0 and JESD204B. The platform allows designers to leverage the power of the NVIDIA Holoscan ecosystem while taking advantage of the PolarFire FPGAs power-efficient technology with low-latency communication and multi-protocol sensor support.
NVIDIA Holoscan helps streamline the development and deployment of AI and high-performance computing HPC applications at the edge for real-time insights. It brings into a single platform the necessary hardware and software systems for low-latency sensor streaming and network connectivity. The platform includes optimised libraries for data processing, sample AI models for jump-starting AI inference pipeline development, template applications to facilitate rapid prototyping and core microservices to run streaming, imaging and other applications.
With its ability to bridge real-time sensor data to NVIDIA Holoscan and the NVIDIA IGX and NVIDIA Jetson platforms for edge AI and robotics, the PolarFire FPGA ethernet sensor bridge unlocks new edge-to-cloud applications, enables AI/ML inferencing and facilitates the adoption of AI in the medical, industrial and automotive markets.
The Ethernet sensor bridge is based on Microchip's highly power-efficient, secure and reliable PolarFire FPGA platform, said Bruce Weyer, vice president of Microchips FPGA business unit. By combining our flexible FPGA fabric with NVIDIAs advanced AI platform and multi-protocol support, were empowering developers to create innovative, real-time solutions that will revolutionise sensor interfaces across a wide range of powerful AI-driven edge applications.
