Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Programmable logic device FPGA has characteristics of flexible design, high efficiency design, short development cycle and low cost. The por of FPGA is divided into static por and dynamic por. The static por is mainly decided by the temperature, it is easy to get por by building a simple model. But the dynamic por is decided by many kinds of design resources. This article focuses on XC4VSX55 FPGA chip and studies the process of its por model, especially proposes a BP neural network algorithm combines adaptive learning rate algorithm with Levenberg-Marquardt. Specific implementation is as follows, firstly, gets the sample data provided by XPor Estimator, then trains the sample data by BP neural network, and estimates the intrinsic links through the ights and thresholds of network, finally, obtains the por model. This article provides a por model, and the improved BP network algorithm not only ensures the accuracy of the training results, but also improves the convergence speed.
['Guochang Zhou', 'Baolong Guo', 'Xiang Gao', 'Jing Ma', 'Hongjie He', 'Yunyi Yan']
I am writing a research paper on a idea A FPGA Power Estimation Method Based on an Improved BP Neural Network
[{"from": "human", "value": "I am writing a research paper on a idea A FPGA Power Estimation Method Based on an Improved BP Neural Network"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Programmable logic device FPGA has characteristics of flexible design, high efficiency design, short development cycle and low cost. The por of FPGA is divided into static por and dynamic por. The static por is mainly decided by the temperature, it is easy to get por by building a simple model. But the dynamic por is decided by many kinds of design resources. This article focuses on XC4VSX55 FPGA chip and studies the process of its por model, especially proposes a BP neural network algorithm combines adaptive learning rate algorithm with Levenberg-Marquardt. Specific implementation is as follows, firstly, gets the sample data provided by XPor Estimator, then trains the sample data by BP neural network, and estimates the intrinsic links through the ights and thresholds of network, finally, obtains the por model. This article provides a por model, and the improved BP network algorithm not only ensures the accuracy of the training results, but also improves the convergence speed."}]