CPU architecture for a fast and energy-saving calculation of convolution neural networks

Florian J. Knoll; Michael Grelcke; Vitali Czymmek; Tim Holtorf; Stephan Hussmann

doi:10.1117/12.2270290

26 June 2017 CPU architecture for a fast and energy-saving calculation of convolution neural networks

Florian J. Knoll, Michael Grelcke, Vitali Czymmek, Tim Holtorf, Stephan Hussmann

Author Affiliations +

Proceedings Volume 10334, Automated Visual Inspection and Machine Vision II; 103340P (2017) https://doi.org/10.1117/12.2270290
Event: SPIE Optical Metrology, 2017, Munich, Germany

Abstract

One of the most difficult problem in the use of artificial neural networks is the computational capacity. Although large search engine companies own specially developed hardware to provide the necessary computing power, for the conventional user only remains the state of the art method, which is the use of a graphic processing unit (GPU) as a computational basis. Although these processors are well suited for large matrix computations, they need massive energy. Therefore a new processor on the basis of a field programmable gate array (FPGA) has been developed and is optimized for the application of deep learning. This processor is presented in this paper. The processor can be adapted for a particular application (in this paper to an organic farming application). The power consumption is only a fraction of a GPU application and should therefore be well suited for energy-saving applications.

Citation Download Citation

Florian J. Knoll, Michael Grelcke, Vitali Czymmek, Tim Holtorf, and Stephan Hussmann "CPU architecture for a fast and energy-saving calculation of convolution neural networks", Proc. SPIE 10334, Automated Visual Inspection and Machine Vision II, 103340P (26 June 2017); https://doi.org/10.1117/12.2270290

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE