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A parallel online regularized least-squares machine learning algorithm for future multi-core processors

Tapio Pahikkala, Antti Airola, Thomas Canhao Xu, Pasi Liljeberg, Hannu Tenhunen, Tapio Salakoski, A parallel online regularized least-squares machine learning algorithm for future multi-core processors. In: César Benavente-peces, Joaquim Filipe (Eds.), Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems (PECCS 2011), 590-599, SciTePress, 2011.

Abstract:

In this paper we introduce a machine learning system based on parallel online regularized least-squares learning algorithm implemented on a network on chip (NoC) hardware architecture. The system is specifically suitable for use in real-time adaptive systems due to the following properties it fulfills. Firstly, the system is able to learn in online fashion, a property required in almost all real-life applications of embedded machine learning systems. Secondly, in order to guarantee real-time response in embedded multi-core computer architectures, the learning system is parallelized and able to operate with a limited amount of computational and memory resources. Thirdly, the system can learn to predict several labels simultaneously which is beneficial, for example, in multi-class and multi-label classification as well as in more general forms of multi-task learning. We evaluate the performance of our algorithm from 1 thread to 4 threads, in a quad-core platform. A Network-on-Chip platform is chosen to implement the algorithm in 16 threads. The NoC consists of a 4x4 mesh. Results show that the system is able to learn with minimal computational requirements, and that the parallelization of the learning process considerably reduces the required processing time.

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BibTeX entry:

@INPROCEEDINGS{iPaAiXuLiTeSa11a,
  title = {A parallel online regularized least-squares machine learning algorithm for future multi-core processors},
  booktitle = {Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems (PECCS 2011)},
  author = {Pahikkala, Tapio and Airola, Antti and Xu, Thomas Canhao and Liljeberg, Pasi and Tenhunen, Hannu and Salakoski, Tapio},
  editor = {Benavente-peces, César and Filipe, Joaquim},
  publisher = {SciTePress},
  pages = {590-599},
  year = {2011},
  keywords = {Regularized least-squares, parallel computation, online learning, network on chip, embedded systems},
}

Belongs to TUCS Research Unit(s): Turku BioNLP Group, Communication Systems (ComSys)

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