Microchip Technology has introduced the VectorBlox™ 3.0 Accelerator Software Development Kit (SDK), a free development platform designed to simplify FPGA-based artificial intelligence implementation and accelerate time-to-market for edge AI applications. Integrated with the CoreVectorBlox IP, the SDK streamlines the optimization, compilation, and deployment of convolutional neural network (CNN) models on PolarFire® FPGA and SoC platforms, enabling developers to consolidate multiple vision and sensor-based AI workloads onto a single low-power FPGA. Leveraging sparsity-based model compression technology acquired through Neuronix, VectorBlox 3.0 supports sparse neural networks by eliminating zero-valued operations, reducing compute and memory requirements while maintaining model accuracy and improving inference performance for power-efficient, always-on edge AI systems. The platform is designed to support demanding applications across industrial, aerospace, defense, and space sectors, where reliability, security, and energy efficiency are critical.
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It also powers advanced capabilities such as Spacecraft Pose Network v2 (SPNv2), enabling autonomous navigation, satellite inspection, debris removal, and formation flying on radiation-resistant PolarFire devices with secure boot and anti-tamper protection. “As AI models continue to grow in complexity, compression is becoming essential for deploying intelligence at the edge,” said Shakeel Peera, corporate vice president and GM of Microchip’s FPGA business unit. “With VectorBlox 3.0, we’re leveraging sparsity-based model compression from our Neuronix acquisition to reduce compute demands while preserving accuracy.” “The combination of PolarFire SoC and VectorBlox creates a powerful synergy for deploying AI-powered autonomy solutions directly in orbit,” said Federico Fontana, Head of Hardware Engineering at AIKO. “We validated this through the deployment of our clear_CHARLES suite, which provides onboard cloud and ship detection for adaptive and autonomous payload operations on power-efficient platforms, making a further step toward increasingly autonomous, responsive and software-defined space systems.”