An intelligent embedded system for real-time adaptive extreme learning machine

Extreme learning machine (ELM) is an emerging approach that has attracted the attention of the research community because it outperforms conventional back-propagation feed-forward neural networks and support vector machines (SVM) in some aspects. ELM provides a robust learning algorithm, free of local minima, suitable for high speed computation, and less dependant on human intervention than the above methods. ELM is appropriate for the implementation of intelligent embedded systems with real-time learning capability. Moreover, a number of cutting-edge applications demanding a high performance solution could benefit from this approach. In this work, a scalable hardware/software architecture for ELM is presented, and the details of its implementation on a field programmable gate array (FPGA) are analyzed. The proposed solution provides high speed, small size, low power consumption, autonomy, and true capability for real-time adaptation (i.e. the learning stage is performed on-chip). The developed system is able to deal with highly demanding multiclass classification problems. Two real-world applications are presented, a benchmark problem of the Landsat images database, and a novel driver identification system for smart car applications. Experimental results that validate the proposal are provided.