Using the VectorBlox Accelerator Software Development Kit to Create Programmable AI/ML Applications in Radiation Tolerant (RT) PolarFire FPGAs

In this paper, we describe the VectorBlox Accelerator software development kit (SDK) and how it is used to optimize and convert trained Artificial Intelligence (AI) models, targeting power-optimized 28nm FPGAs. Neural networks are sourced from a variety of supported input frameworks, such as TensorFlow, Caffe, ONNX, and PyTorch. The VectorBlox Accelerator SDK performs a three-step conversion flow to optimize the networks, calibrate and scale them to 8-bit representation, and finally create an image for implementation on the FPGA. Power-efficient implementation of the neural network on the FPGA is achieved by a soft IP core called CoreVectorBlox. This soft IP core comprises a RISC-V processor and firmware, a vector processor, and a convolutional neural network accelerator, which consists of a two-dimensional array of processing elements, making use of the multiply-accumulate blocks in the RT PolarFire FPGA. By implementing neural networks in a matrix processor programmed in the fabric of the new radiation-tolerant RT PolarFire FPGA, the networks can be iterated and changed without resynthesizing the FPGA, resulting in convenient, programmable low-power AI applications that can be dynamically changed at run-time. Examples of performance and utilization of a variety of neural networks sourced from TensorFlow, Caffe, ONNX and PyTorch will be provided, for implementations both with and without triple module redundancy which may be desired for radiation mitigation purposes. The radiation-tolerant RT PolarFire FPGA will be described, with emphasis on radiation test data and schedules for qualification and flight models