MPQA: Mixed-Precision Quantization Accelerator for CNN Inference

Mixed-precision quantization CNNs have become crucial for edge vision algorithms. While model quantization techniques have improved inference efficiency, existing approaches have not fully addressed data coherence in coarse-grained parallelism or adaptation to various 2D data sizes. This study presents a novel CNN accelerator equipped with mixed-precision multiplier and feature linking mechanisms to enhance mixed-precision inference on edge NPUs. The accelerator incorporates reconfigurable multi-precision multiplication support in MAC units, enabling 8-bit/4-bit mixed-precision quantization. A feature linking mechanism is developed to overcome feature map size limitations, achieving scalable processing dimensions. The accelerator design, implemented on a Xilinx Zynq UltraScale+ MPSoC FPGA platform, demonstrates improved hardware performance and enhanced inference speeds for edge device CNN models such as Tiny-YOLOv3 and ResNet18. This work provides a significant advancement in deploying mixed-quantization CNNs on edge NPUs, enhancing the adaptability and performance of edge computing devices for complex neural network models.