Efficient architecture for improving differential equations based on normal equation method in deep learning

Abstract

Deep learning has been employed to build applications and greatly promoted the development of the industries from many areas. Among the deep learning algorithms, normal equation method is widely used and is very time-consuming. Thus, it is very urgent to improve normal equation method. First, we propose a systolic Gaussian elimination. Second, we propose a systolic Gauss-Jordan elimination. By integrating other designs, we build an efficient architecture for improving differential equations in normal equation method. We implement our design in the development environment of artificial intelligence, which shows that it is very efficient for deep learning and its applications.

Keywords

• Deep learning
• Normal equation method
• Differential equation
• Systolic architecture
• Artificial intelligence