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arXiv · 2504.04884

Parallelization is All System Identification Needs: End-to-end Vibration Diagnostics on a multi-core RISC-V edge device

Amirhossein Kiamarzi·Amirhossein Moallemi·Federica Zonzini·Davide Brunelli·Davide Rossi·Giuseppe Tagliavini
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Abstract

The early detection of structural malfunctions requires the installation of real-time monitoring systems ensuring continuous access to the damage-sensitive information; nevertheless, it can generate bottlenecks in terms of bandwidth and storage. Deploying data reduction techniques at the edge is recognized as a proficient solution to reduce the system's network traffic. However, the most effective solutions currently employed for the purpose are based on memory and power-hungry algorithms, making their embedding on resource-constrained devices very challenging; this is the case of vibration data reduction based on System Identification models. This paper presents PARSY-VDD, a fully optimized PArallel end-to-end software framework based on SYstem identification for Vibration-based Damage Detection, as a suitable solution to perform damage detection at the edge in a time and energy-efficient manner, avoiding streaming raw data to the cloud. We evaluate the damage detection capabilities of PARSY-VDD with two benchmarks: a bridge and a wind turbine blade, showcasing the robustness of the end-to-end approach. Then, we deploy PARSY-VDD on both commercial single-core and a specific multi-core edge device. We introduce an architecture-agnostic algorithmic optimization for SysId, improving the execution by 90x and reducing the consumption by 85x compared with the state-of-the-art SysId implementation on GAP9. Results show that by utilizing the unique parallel computing capabilities of GAP9, the execution time is 751{\mu}s with the high-performance multi-core solution operating at 370MHz and 0.8V, while the energy consumption is 37{\mu}J with the low-power solution operating at 240MHz and 0.65V. Compared with other single-core implementations based on STM32 microcontrollers, the GAP9 high-performance configuration is 76x faster, while the low-power configuration is 360x more energy efficient.

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BibTeXRIS

Amirhossein Kiamarzi, Amirhossein Moallemi, Federica Zonzini, Davide Brunelli, Davide Rossi, Giuseppe Tagliavini. 2025-04-07. Parallelization is All System Identification Needs: End-to-end Vibration Diagnostics on a multi-core RISC-V edge device. https://arxiv.org/abs/2504.04884

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