Parallelized Online Regularized Least-Squares for Adaptive Embedded Systems

Parallelized Online Regularized Least-Squares for Adaptive Embedded Systems

Tapio Pahikkala (University of Turku, Finland), Antti Airola (University of Turku, Finland), Thomas Canhao Xu (University of Turku, Finland), Pasi Liljeberg (University of Turku, Finland), Hannu Tenhunen (University of Turku, Finland) and Tapio Salakoski (University of Turku, Finland)
DOI: 10.4018/jertcs.2012040104
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The authors introduce a machine learning approach based on parallel online regularized least-squares learning algorithm for parallel embedded hardware platforms. The system is suitable for use in real-time adaptive systems. Firstly, the system can learn in online fashion, a property required in real-life applications of embedded machine learning systems. Secondly, 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 predict several labels simultaneously. The authors evaluate the performance of the algorithm from three different perspectives. The prediction performance is evaluated on a hand-written digit recognition task. The computational speed is measured from 1 thread to 4 threads, in a quad-core platform. As a promising unconventional multi-core architecture, Network-on-Chip platform is studied for the algorithm. The authors construct a NoC consisting of a 4x4 mesh. The machine learning algorithm is implemented in this platform with up to 16 threads. It is shown that the memory consumption and cache efficiency can be considerably improved by optimizing the cache behavior of the system. The authors’ results provide a guideline for designing future embedded multi-core machine learning devices.
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The design of adaptive systems is an emerging topic in the area of pervasive and embedded computing. Rather than exhibiting pre-programmed behavior, it would in many applications be beneficial for systems to be able to adapt to their environment. Isoaho et al. (2010) analyze current key challenges in developing embedded systems. One of the outlined main challenges is self-awareness, meaning that a system should be able to monitor its environment and own state and based on this optimize its behaviour in order to meet service quality criteria. System security is outlined as another major challenge, as is making best use of parallelism that is increasingly present in modern embedded systems.

In order to meet these goals, a system should automatically learn to model its environment in order to choose correct actions, and over time improve its performance as more feedback is gained. Automatically constructed mathematical model of a system may be used to predict the future behaviour, given as input the current state and planned actions, an approach known as model predictive control. The behaviour patterns of software or human agents may be automatically observed in order to recognize possible security risks. And even more, imagine smart music players that adapt to the musical preferences of their owner, intelligent traffic systems that monitor and predict traffic conditions and re-direct cars accordingly, etc. Thus we motivate the need for a generic approach to learning predictive models in embedded environments, which are typically characterized by properties such as need for fast (and constant) response times, limited amount of memory resources and parallel computing architecture.

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