FPGA SDK enables sparse AI models
Version 3.0 of Microchip’s VectorBlox Accelerator SDK simplifies FPGA-based AI implementation by supporting sparse neural networks. Available free of charge, the VectorBlox SDK and associated CoreVectorBlox IP form an integrated toolchain that streamlines the optimization, compilation, and deployment of convolutional neural network (CNN) models on PolarFire FPGA and SoC platforms.
Designed to scale across different model sizes and multiple AI workloads, VectorBlox enables customers to consolidate vision and sensor AI functions on a single low-power FPGA. Sparsity-based model compression reduces compute and memory requirements by skipping zero-valued operations, improving inference performance while lowering power consumption.
VectorBlox SDK 3.0 integrates with Microchip’s Libero SoC Design Suite and provides broad AI model support for TensorFlow, TensorFlow Lite, ONNX, and OpenVINO.
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