A Comparative Analysis of HDL and HLS for Accelerating Machine Learning based Strain Estimation with Ultrasonic Guided Waves - Mendes, Davi A.; Reves, Gabriel; Pastrana, M. A.; Domingues, Pedro H.; Ayala, Helon V. H.; Kubrusly, Alan C.; Muñoz, Daniel M.; Llanos, Carlos H. (Anais do Simpósio Brasileiro de Engenharia de Sistemas Computacionais (SBESC); 2023: Anais do XIII Simpósio Brasileiro de Engenharia de Sistemas Computacionais, 2023)
In the field of nondestructive testing and structural health monitoring, ultrasonic waves are widely utilized to identify defects and characterize materials. Recently, data-driven machine learning models have been proposed for strain estimation using shallow-models and Principal Component Analysis (PCA). However, little research effort has been guided towards the development of real-time strain estimation hardware accelerators. This study presents a novel comparative analysis of hardware implementations of PCA on a low-cost SoC-FPGA using High-level Synthesis (HLS) and HDL-based architectures. The comparison was conducted in terms of relevant metrics: hardware occupation, latency, and computational efficiency. Additionally, we demonstrate a scalability analysis considering floating-point bit-width representation and the number of operators. The proposed HDL-based architecture was able to achieve similar performance in comparison with the HLS-based implementation. The advantages of the proposed hardware accelerators are shown by their real-time inference capabilities, low power consumption, and reduced hardware utilization associated with low latency and elevated computational efficiency.