现场可编程门阵列
计算机科学
分割
声音(地理)
深度学习
嵌入式系统
人工智能
语音识别
实时计算
工程类
声学
物理
作者
Daniel Enériz,Antonio J. Rodríguez-Almeida,Himar Fabelo,Samuel Ortega,Francisco Balea-Fernandez,Gustavo M. Callicó,N. Medrano,B. Calvo
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-1
被引量:3
标识
DOI:10.1109/tim.2024.3392271
摘要
The development of real-time, reliable, low-cost automatic Phonocardiogram (PCG) analysis systems is critical for early detection of Cardiovascular Diseases (CVDs), especially in countries with limited access to primary health care programs.Once the raw PCG acquired by the stethoscope has been preprocessed, the first key task is its segmentation into the fundamental heart sounds.For this purpose, an optimized hardware implementation of the segmentation algorithm is essential to attain a computer-aided diagnostic system based on PCGs.This paper presents the optimization of a U-Net-based segmentation algorithm for its implementation in a low-end Field-Programmable Gate Array (FPGA) using low-resolution fixed-point data types.The optimization strategies seek to reduce the system latency while maintaining a constrained consumption of FPGA resources, aiming for a real-time response from the stethoscope data acquisition to the CVDs detection.Experimental results prove a 64% decrease in latency compared to a baseline version, a 3.9% reduction of Block Random Access Memory, which is the limiting resource of the design, and a 70% reduction in energy consumption.To the best of our knowledge, this is the first work to exhaustively study different optimization strategies for implementing a large 1D U-Net-based model, achieving realtime fully characterized performance.
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