A Novel Technique to Support Deep Learning Applications in a Model-Based Embedded Software Design Methodology

Abstract

As deep learning applications are getting popular in embedded systems, how to support deep learning applications in the model-based embedded software design methodology becomes a challenging problem. A previous solution is to represent each deep learning application with a model. However, it requires significant efforts to translate specifications and obtain good performance by applying optimization techniques to deep learning applications. In this work, we propose a novel methodology that leverages the benefits of using deep learning software development kit (SDK) for performance optimization. In the proposed methodology, we first obtain the Pareto-optimal mapping solutions of deep learning applications using the SDK associated with the hardware platform. Afterward, we perform mapping of dataflow tasks and selection of mapping solutions of deep learning (DL) applications together through a genetic algorithm. Experiments with a real-life example and randomly generated graphs show that we could reduce at least 5% of the maximum utilization compared to our previous work that maps DL applications and dataflow applications sequentially.

Keywords

• Model-based software design
• mapping exploration
• deep learning applications
• heterogeneous processors