Abstract
This abstract highlights challenges in machine learning accelerator design and proposes solutions through software/hardware co-design techniques. To optimize single object detection, we introduce Mask-Net, a lightweight network that eliminates redundant computation. To address hardware limitations in Dynamic Graph Neural Networks (DGNNs), we present DGNN-Booster, a graph-agnostic FPGA accelerator. Our designs are open-source, generic, and applicable to real-world scenarios.