التفاصيل البيبلوغرافية
| العنوان: |
A Digital Implementation of Extreme Learning Machines for Resource-Constrained Devices. |
| المؤلفون: |
Ragusa, Edoardo, Gianoglio, Christian, Gastaldo, Paolo, Zunino, Rodolfo |
| المصدر: |
IEEE Transactions on Circuits & Systems. Part II: Express Briefs; Aug2018, Vol. 65 Issue 8, p1104-1108, 5p |
| مستخلص: |
The availability of compact digital circuitry for the support of neural networks is a key requirement for resource-constrained embedded systems. This brief tackles the implementation of single hidden-layer feedforward neural networks, based on hard-limit activation functions, on reconfigurable devices. The resulting design strategy relies on a novel learning procedure that inherits the approach adopted in the Extreme Learning Machine paradigm. The eventual training process balances accuracy and network complexity effectively, thus supporting a digital architecture that prioritizes area utilization over computational performance. Experimental tests confirm that the design approach leads to efficient digital implementations of the predictor on low-performance devices. [ABSTRACT FROM AUTHOR] |
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Copyright of IEEE Transactions on Circuits & Systems. Part II: Express Briefs is the property of IEEE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
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