Ph.D. Project: Accelerated Machine Learning for On-Orbit OPIR Target Detection

Detecting dim point-source targets in overhead persistent infrared (OPIR) imagery with high probability requires emerging machine-learning (ML) methods. These complex algorithms and increasing sensor resolution have made high-throughput, on-orbit processing challenging. This research ex-plores end-to-end acceleration of the Multiscale Local Contrast Learning Network (MLCLNet) for scalable on-orbit target detection with arbitrarily large input frames. An end-to-end pipeline for inference of batched subframes is developed and various inference accelerators are explored including GPU, Xilinx Deep Learning Processor Unit (DPU), and a custom FPGA design. The target detection performance for quantized MLCLNet is evaluated at various model sizes. The end-to-end pipeline is evaluated in terms of throughput, latency, resources, power, and efficiency for each inference accelerator considered. Initial results for FPGA-based acceleration using the Xilinx DPU are presented.