A Matrix-Multiplying ADC Implementing a Machine-Learning Classifier Directly with Data Conversion

📋 论文概要

本文提出了一种矩阵乘法ADC，将机器学习分类器的特征提取和分类合并为一个矩阵运算，直接在数据转换过程中实现分类，从而减少所需的乘法次数。该方法针对嵌入式传感系统中信号分析复杂的问题，利用数据驱动的机器学习替代传统解析模型。

💡 主要创新点

• 提出一种算法公式，将特征提取和分类合并为单个矩阵，减少总乘法次数。
• 设计一种矩阵乘法ADC（MMADC），直接在数据转换阶段完成矩阵运算和分类。

🏷 关键词

📄 原文摘要

Embedded sensing systems conventionally perform A-to-D conversion followed by signal analysis. In many applications, the analysis of interest is inference (e.g., classification), but the sensor signals involved are too complex to model analytically. Machine learning is gaining prominence because it enables datadriven training of classifiers, overcoming the need for analytical models. This work presents: 1) an algorithmic formulation, where feature extraction and classification are combined into a single matrix, reducing the total multiplications needed, and 2) a matrix-multiplying ADC (MMADC) that enables multiplication of input samples by a programmable matrix. Thus, the MMADC combines feature extraction and classification with data conversion, mitigating the need for further computations. Two systems are demonstrated: an ECG-based cardiac-arrhythmia detector and an image-pixel-based gender

👥 作者与机构

Jintao Zhang, Zhuo Wang, Naveen Verma

Princeton University, Princeton, NJ