A Parallel Online Regularized Least-squares Machine Learning Algorithm for Future Multi-core Processors.
2011 (English)In: PECCS 2011 - Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems 2011, 2011, 590-599 p.Conference paper (Refereed)
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 4×4 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.
Place, publisher, year, edition, pages
2011. 590-599 p.
Machine learning; Network-on-Chip; Online learning; Regularized least-squares
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-73388ISBN: 978-989842548-5OAI: oai:DiVA.org:kth-73388DiVA: diva2:488860
1st International Conference on Pervasive and Embedded Computing and Communication Systems, PECCS 2011, Vilamoura, Algarve, 5-7 March, 2011
QC 201202022012-02-022012-02-022012-02-02Bibliographically approved