糖尿病疾病预测投票分类算法的低功耗模拟集成架构
IEEE transactions on biomedical circuits and systems
|July 2, 2024
概括
本研究介绍了一种用于投票分类算法的低功耗模拟集成架构,以最小的能源消耗实现高精度. 它有效地处理多个输入,用于疾病检测等应用.
科学领域:
- 模拟集成电路的模拟集成电路
- 机器学习硬件 机器学习硬件
- 生物医学信号处理
背景情况:
- 开发能效分类器对于便携式和可植入的医疗设备至关重要.
- 现有的模拟分类器经常面临准确性,功耗和功能处理能力之间的权衡.
研究的目的:
- 为多功能投票分类算法引入一种全新的,完全模拟的集成架构.
- 为了证明高精度和极低功耗 (600nW) 的多输入功能分类.
主要方法:
- 设计了一个多功能投票算法,集成贝叶斯,Centroid和学习向量量定量模型.
- 使用高斯概率,欧几里德距离和电流比较电路实施了分类模型.
- 使用TSMC 65nm CMOS 技术和Cadence IC Suite进行电路设计和模拟.
主要成果:
- 在现实生活中的糖尿病数据集上实现了高分类准确性.
- 证明了600nW的异常低功耗.
- 与流行的模拟分类器和基于软件的模型进行性能比较.
结论:
- 拟议的模拟集成架构为低功耗,高精度的分类任务提供了有前途的解决方案.
- 多功能投票方法提高了对各种输入特征的适应性.
- 这种设计适用于资源有限的应用,包括医疗诊断.
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