Journal article 397 views 286 downloads
A parallel implementation of sequential minimal optimization on FPGA
Daniel H. Noronha,
Matheus Torquato 
,
Marcelo A.C. Fernandes
,
Marcelo A.C. Fernandes
Microprocessors and Microsystems, Volume: 69, Pages: 138 - 151
Swansea University Author:
Matheus Torquato
-
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DOI (Published version): 10.1016/j.micpro.2019.06.007
Abstract
This paper proposes a parallel FPGA implementation of the training phase of a Support Vector Machine (SVM). The training phase of the SVM is implemented using Sequential Minimal Optimization (SMO), which enables the resolution of a complex convex optimization problem using simple steps. The SMO impl...
| Published in: | Microprocessors and Microsystems |
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| ISSN: | 0141-9331 |
| Published: |
2019
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa50890 |
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| Abstract: |
This paper proposes a parallel FPGA implementation of the training phase of a Support Vector Machine (SVM). The training phase of the SVM is implemented using Sequential Minimal Optimization (SMO), which enables the resolution of a complex convex optimization problem using simple steps. The SMO implementation is also highly parallel and uses some acceleration techniques, such as the error cache. Moreover, the Hardware Friendly Kernel (HFK) is used in order to reduce the kernel’s area, enabling an increase in the number of kernels per area. After the parallel implementation in hardware, the SVM is validated by bit-accurate simulation. Finally, analysis associated with the temporal performance of the proposed structure, as well as analysis associated with FPGAs area usage is performed. |
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| Keywords: |
SVM, SMO, FPGA, Support vector machine, Sequential minimal optimization, Hardware |
| College: |
Faculty of Science and Engineering |
| Start Page: |
138 |
| End Page: |
151 |