Incremental Learning for Real-time Partitioning for FPGA Applications

The co-design approach consists in defining all the sub-tasks of an application to be integrated and distributed on software or hardware targets. The introduction of conventional cognitive reasoning can solve several problems such as real-time hardware/software classification for FPGA-based applications. However, this requires the availability of large databases, which may conflict with real-time applications. The proposed method is based on the Incremental Kernel SVM (InKSVM) model. InKSVM learns incrementally, as new data becomes available over time, in order to efficiently process large, dynamic data and reduce computation time. As a result, it relaxes the assumption of complete data availability and provides fully autonomous performance. Hence, in this paper, an incremental learning algorithm for hardware/software partitioning is presented. Starting from a real database collected from our FPGA experiments, the proposed approach uses InKSVM to perform the task classification in hardware and software. The proposal has been evaluated in terms of classification efficiency. The performance of the proposed approach was also compared to reference works in the literature. The results of the evaluation consist in empirical evidence of the superiority of the InKSVM over state-of-the-art progressive learning approaches in terms of model accuracy and complexity.