Abstract: Classifying respiratory sounds is essential for the early diagnosis of respiratory diseases. However, the computational cost of existing computer-aided respiratory sound analysis approaches limits their practical deployment in real-time clinical environments. This study presents embedded PANNs-based intelligent auscultation with XGBoost (EPIX), an intelligent embedded auscultation instrumentation system that integrates an electronic stethoscope interface, a PANNs-based acoustic feature extraction module, an optimized eXtreme gradient boosting (XGBoost) classifier, and a two-stage decision mechanism for reliable real-time respiratory sound classification and measurement. For hardware implementation, EPIX integrates a Raspberry Pi 5 and a dedicated expansion module, enabling real-time inference for electronic stethoscopes and batch inference through an external USB drive, providing flexible processing capabilities. EPIX achieved an SC score of 0.7618 on the SPRSound dataset, while significantly reducing computational resource requirements. These capabilities make EPIX well-suited for embedded measurement scenarios with limited computational resources. The processing workflow on the device provides consistent measurement performance and enables practical use in portable and bedside intelligent auscultation instruments.