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

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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Paper Citation


in Harvard Style

Pahikkala T., Airola A., Canhao Xu T., Liljeberg P., Tenhunen H. and Salakoski T. (2011). A PARALLEL ONLINE REGULARIZED LEAST-SQUARES MACHINE LEARNING ALGORITHM FOR FUTURE MULTI-CORE PROCESSORS . In Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: SAAES, (PECCS 2011) ISBN 978-989-8425-48-5, pages 590-599. DOI: 10.5220/0003411405900599


in Bibtex Style

@conference{saaes11,
author={Tapio Pahikkala and Antti Airola and Thomas Canhao Xu and Pasi Liljeberg and Hannu Tenhunen and Tapio Salakoski},
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 - Volume 1: SAAES, (PECCS 2011)},
year={2011},
pages={590-599},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003411405900599},
isbn={978-989-8425-48-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: SAAES, (PECCS 2011)
TI - A PARALLEL ONLINE REGULARIZED LEAST-SQUARES MACHINE LEARNING ALGORITHM FOR FUTURE MULTI-CORE PROCESSORS
SN - 978-989-8425-48-5
AU - Pahikkala T.
AU - Airola A.
AU - Canhao Xu T.
AU - Liljeberg P.
AU - Tenhunen H.
AU - Salakoski T.
PY - 2011
SP - 590
EP - 599
DO - 10.5220/0003411405900599