Significant upgrades have been rolled out to Microchip Technology’s VectorBlox Accelerator software development kit (SDK) for convolutional neural network (CNN)-based artificial intelligence/machine learning (AI/ML) inference with PolarFire FPGAs. Along with the recent release of version 1.3 comes 19 new tutorials, bringing the total to 37. These include licence plate recognition in TensorFlow, MobileNet v1 and v2 in TensorFlow and TensorFlow2, various ResNet and SqueezeNet models in ONNX, torchvision ResNet in PyTorch and TinyYoloV4 in TensorFlow.
Network sizes have been reduced by 50% by optimising containers for weights and the built-in bit accuracy simulator’s speed has been tripled compared to version 1.2. Other dependencies that have been upgraded in the SDK include OpenVINO version 2021.4.1 and Python version 22.214.171.124. Microchip also upgraded the Libero software example project and programming file to support Libero software version 2021.2 and hot-swappable camera and HDMI inputs.
The company also added a new application notes section in GitHub that covers the following topics:
• Training custom Yolo V2/V3 networks via DarkNet.
• Running custom Yolo V2/V3 networks.
• Inspecting and converting TensorFlow 2 models.
• Face recognition: summary of the demo design.
• Face recognition: changing the faces database to identify other faces.
Powered by VectorBlox Accelerator SDK version 1.3, PolarFire FPGAs offer two to three times more power-efficient inference in mid-range FPGAs. This enables ML inference in applications that are thermally constrained and operates on battery power in environments that are susceptible to single-event upsets such as those experienced in low-orbit aerospace.
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