A Comparative Analysis of HDL and HLS for Accelerating Machine Learning based Strain Estimation with Ultrasonic Guided Waves

• Davi A. Mendes UnB
• Gabriel Reves UnB
• M. A. Pastrana UnB
• Pedro H. Domingues PUC-Rio
• Helon V. H. Ayala PUC-Rio
• Alan C. Kubrusly PUC-Rio
• Daniel M. Muñoz UnB
• Carlos H. Llanos UnB

Resumo

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.

Palavras-chave: FPGA, HLS, Principal Component Analysis, Machine Learning, Ultrasonic Guided Waves, Strain Estimation