Abstract: Image preprocessing is a critical component in machine learning tasks,and its computational efficiency directly impacts the performance of model training and inference.Traditional CPU computations struggle to meet the real-time processing demands of high-resolution images and large-scale datasets,whereas Neural Processing Units(NPUs),with their high performance,emerge as ideal platforms for accelerating image preprocessing.However,the diversity and memory access intensiveness of image preprocessing operations do not fully align with the matrix operation optimization mode of NPUs,posing significant challenges for their adaptation.Based on the Phytium CPU and Cambricon MLU heterogeneous computing platform,this paper proposes an efficient acceleration method for image preprocessing operators.By thoroughly analyzing the multi-core parallel architecture and sto-rage system of the MLU,the computational logic and task partitioning of the operators are redesigned.Combined with optimization strategies such as multi-core parallel scheduling,vectorized computation,three-level storage structure,and double buffering mechanism,the computational performance of the operators is significantly enhanced,demonstrating the potential of NPUs in accelerating image preprocessing.Experimental results show that the optimized ten common image preprocessing operators achieve a performance improvement of 33.22% to 234.46% compared to the native operators in PaddlePaddle.In deep learning and traditional machine learning tasks,end-to-end performance improvements of 12.47% and 48.63% are achieved,respectively.

Key words: Image preprocessing, Cambrian MLU, High performance computing, Parallel computing, Performance optimization