Method and system for predicting operation time of sparse matrix vector multiplication

US12242563B2 · kind B2 · utility

Assignees

• China Institute of Atomic Energy · CN
• COMPUTER NETWORK INFORMATION CENTER, CHINESE ACADEMY OF SCIENCES · CN

Inventors

• Jue Wang · Fairport, US
• Yangde Feng · Beijing, CN
• Yangang Wang · Beijing, CN
• Zhongxiao Cao · Beijing, CN
• Wen-Feng Yang · Beijing, CN
• Tiancai Liu · Beijing, CN
• Ningming Nie · Beijing, CN
• Fuhai Gao · Beijing, CN
• Xiaoguang Wang · Hefei, CN
• Yue Gao · 红钢城街道, CN

Key dates

• Filing date: Nov 16, 2020
• Grant date: Mar 4, 2025
• Expiry date: Apr 2, 2043

Classification

• Technology area (CPC Y): Emerging Cross-Sectional Technologies
• CPC primary: Y04S10/50
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

The disclosure relates to a method and a system for predicting the operation time of sparse matrix vector multiplication. The method comprises constructing a convolutional neural network comprising an input layer, a feature processing layer, a data splicing layer and an output layer for outputting prediction results. The method further comprises acquiring a plurality of groups of sparse matrices with known sparse matrix vector multiplication operation time as sample data, inputting the sample data into the convolutional neural network to train the convolutional neural network, and inputting the sparse matrix to be classified into the trained convolutional neural network to realize the prediction of the operation time of sparse matrix vector multiplication.