Gaussian error function circuit applied to neural networks
A Gaussian error function, neural network technology, applied in biological neural network models, electrical digital data processing, digital data processing components and other directions, can solve the problems of low precision and large area, achieve high circuit precision, small area, promote effect of research
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment Construction
[0028] The specific implementation manners of the present invention will be described in detail below in conjunction with the accompanying drawings.
[0029] At present, there are two common traditional Gaussian error function circuit implementation methods, one is to use the square root function shown in formula (3) to design hardware, where ε(x) represents the error. The Matlab software simulation results of the algorithm are shown in figure 1 . It can be seen that the accuracy of the algorithm is very low, and the maximum absolute error |ε(x)| is 6.3*10 -3 , the accuracy is extremely poor, so it is not suitable for the design of hardware circuits.
[0030]
[0031] Another traditional implementation is to use the Taylor expansion method to perform Taylor expansion on erf(x) in the [-3,3] interval, which can be expressed as:
[0032]
[0033] When n=28, the hardware output error curve of the hardware implementation is as follows: figure 2 shown. Its maximum absol...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com