Performance Evaluation of the Hailo-8L AI Kit for Seismic Signal Denoising Model Inference on the Raspberry Pi 5
Antonia Indriyani Juniar (1a), Ahmad Kadarisman (1,2) , Djati Handoko (1), Martarizal (1,b*)

1 Department of Physics Faculty of Mathematics and Natural Sciences, Universitas Indonesia
2 Direktorat Instrumentasi dan Kalibrasi, BMKG, Jl. Angkasa I/2 Kemayoran - Jakarta, Indonesia

Email: a) antonia.indriyani[at]ui.ac.id , b) martarizal[at]sci.ui.ac.id*
*Corresponding Author

Abstract

Real-time seismic monitoring in urban and remote environments is increasingly challenged by strong anthropogenic noise and limited computational resources at field stations. Although deep learning-based seismic denoising methods have demonstrated promising performance, their deployment on low-power edge AI platforms for continuous onsite monitoring remains insufficiently explored. This study investigates the feasibility of implementing a Conv2D-based Denoising Autoencoder (DAE) for real-time seismic signal denoising on a Raspberry Pi 5 integrated with the Hailo-8L AI accelerator. The model was trained using synthetic noise injection on seismic waveform data from the CIKJI station to generate noisy-clean signal pairs. The deployment pipeline included signal preprocessing, Conv1D-to-Conv2D adaptation, ONNX conversion, and INT8 quantization into the Hailo Execution Format (HEF). Experimental evaluation on 21,598 seismic windows demonstrated that the Hailo INT8 implementation achieved a Pearson correlation of 0.9645 and an SNR of 14.95 dB, compared to 0.9727 and 17.08 dB obtained by CPU FP32 inference, respectively. Despite a modest degradation in denoising accuracy, the Hailo-8L significantly reduced CPU utilization from 48.9% to 12.8% and maintained stable sub-millisecond inference latency during 24-hour simulated streaming tests. These results demonstrate that edge AI accelerators provide a practical tradeoff between denoising performance and computational efficiency, supporting the development of low-power real-time seismic monitoring systems for onsite deployment in resource-constrained environments.

Keywords: Denoising, Autoencoder, Seismic Signal, Hailo 8L, Mini seed, Raspberry Pi 5

Topic: Instrumentation and Computational Physics