Intelligent Layer Sharing (ILASH): A Predictive Neural Architecture Search Framework for Multi-Task Applications

Artificial intelligence (AI) is widely used in various fields including healthcare, autonomous vehicles, robotics, traffic monitoring, and agriculture. Many modern AI applications in these fields are multi-tasking in nature (i.e. perform multiple analyses on the same data) and are deployed on resource-constrained edge devices, requiring the AI models to be efficient across different metrics such as power, frame rate, and size. For these specific use-cases, we propose a new paradigm of intelligent neural network architecture search framework (ILASH) that leverages a layer sharing concept for minimizing power utilization, increasing frame rate, and reducing model size. ILASH utilizes a data-driven intelligent approach to make the search efficient in terms of energy, time, and frames per second (FPS). We perform extensive evaluations of the proposed layer shared architecture paradigm and the ILASH framework using three open-source datasets (UTKFace, MTFL, and CelebA). We compare ILASH with two different neural architecture search libraries that support multi-task applications (LibMTL and AutoKeras). We also evaluate ILASH against two standard neural architecture search frameworks (DARTS and ENAS). ILASH was able to surpass state-of-the-art performance across most comparison metrics (e.g. task accuracy, search/inference energy, and fps).