A Parallel Online Regularized Least-squares Machine Learning Algorithm for Future Multi-core Processors

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 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.