Research on high energy efficiency and low bit-width floating-point type data for abnormal object detection of transmission lines  

作　　者：Chen Wang Guozheng Peng Rui Song Jun Zhang Li Yan 

机构地区：[1]China Electric Power Research Institute,Beijing 100192,P.R.China [2]Information&Telecommunications Company,State Grid Shandong Electric Power Company,Jinan 250001,P.R.China

出　　处：《Global Energy Interconnection》2024年第3期324-335,共12页全球能源互联网（英文版）

基　　金：supported by State Grid Corporation Basic Foresight Project(5700-202255308A-2-0-QZ).

摘　　要：Achieving a balance between accuracy and efficiency in target detection applications is an important research topic.To detect abnormal targets on power transmission lines at the power edge,this paper proposes an effective method for reducing the data bit width of the network for floating-point quantization.By performing exponent prealignment and mantissa shifting operations,this method avoids the frequent alignment operations of standard floating-point data,thereby further reducing the exponent and mantissa bit width input into the training process.This enables training low-data-bit width models with low hardware-resource consumption while maintaining accuracy.Experimental tests were conducted on a dataset of real-world images of abnormal targets on transmission lines.The results indicate that while maintaining accuracy at a basic level,the proposed method can significantly reduce the data bit width compared with single-precision data.This suggests that the proposed method has a marked ability to enhance the real-time detection of abnormal targets in transmission circuits.Furthermore,a qualitative analysis indicated that the proposed quantization method is particularly suitable for hardware architectures that integrate storage and computation and exhibit good transferability.

关 键 词：Power edge Data format Quantification Compute-in-memory 

分 类 号：TM75[电气工程—电力系统及自动化]