Deploying AI inference in powerconstrained and missioncritical environments such
as aerospace and defense systems requires solutions that balance performance, efficiency, reliability and
ease of development. To better manage these challenges, Microchip Technology (Nasdaq: MCHP) has
released the VectorBlox 3.0 Accelerator Software Development Kit (SDK) to help simplify FPGAbased AI
implementation and speed timetomarket. Offered to developers free of charge, VectorBlox 3.0 SDK and
associated CoreVectorBlox IP is designed as an integrated toolchain that streamlines optimization,
compilation and deployment of convolutional neural network (CNN) models on PolarFire FPGA and SoC-
based platforms. Because the accelerator scales efficiently across model sizes and supports multiple AI
workloads on a single device, customers can consolidate various vision or sensorbased AI functions on a
single low power FPGA.
“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.”
With support for sparse neural networks, VectorBlox 3.0 helps enable efficient execution of vision-based
CNN models by skipping zerovalued operations. This capability helps developers accelerate inference
performance while reducing power consumption, an important advantage for alwayson edge AI
applications that must balance responsiveness with energy efficiency. Enabling sparsity-based model
compression is designed to reduce compute and memory demands, while preserving accuracy.
“Leveraging VectorBlox acceleration on Microchip’s PolarFire SoC enabled us to efficiently deploy advanced
onboard AI pipelines for low-latency payload operations in orbit,” said Vito Fortunato, SPACEDGE
services line manager at Planetek Italia. “The platform allowed us to validate real-time Earth Observation
processing capabilities including object detection, semantic scene analysis and edge-generated actionable
information products on top of the AI-eXpress-1 satellite, deployed in 2025, while providing the radiation
resilience and operational reliability required for continuous Low Earth Orbit operations.”
Additionally, Spacecraft Pose Network v2 (SPNv2), a neural network designed to estimate position and
orientation using vision data, enables autonomous navigation and proximity operations in space for
applications such as autonomous rendezvous and docking, space debris removal, satellite inspection and
formation flying. Built on mid-range, power-efficient, single-event-upset (SEU) immune PolarFire FPGAs and
SoCs, the solution delivers secure boot, anti-tamper protection and high reliability for harsh environments.
These features are necessary for missioncritical defense, aerospace and industrial deployments where long
operational life, data protection and system resilience are essential.
“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.”
