EAGER: Low-Energy Architectures for Machine Learning

EAGER：机器学习的低能耗架构

基本信息

批准号：
1749494
负责人：
Keshab Parhi
金额：
$ 12.5万
依托单位：
University of Minnesota-Twin Cities
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-15 至 2018-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1749494&HistoricalAwards=false
关键词：
EAGER Low Energy Architectures Machine

项目摘要

Machine learning systems and classifiers will be part of future smart devices. Industrial internet-of-things (IIOT) and cyber-physical systems (CPS) will be equipped with real-time feature extraction and classification to provide feedback and/or warning signals in some cases. Smart medical devices can analyze signals and trigger therapy to improve human health. Security systems can analyze activity data and thwart planned attacks. Reducing energy consumption in these smart devices is critical for increasing battery life in portable applications. This proposal addresses techniques to reduce energy consumption in feature extraction and classification. The broader impacts will be in demonstrating a new approach for feature extraction and classification with significantly less energy consumption without degrading sensitivity and specificity, along with training and educating graduate and undergraduate students in related disciplines through laboratory and computational experiences.The proposed framework computes features and classifies the test data using a simple level-1 classifier that makes use of low precision. If the classification is successful, then the process terminates. Otherwise the level-2 classifier is invoked. The level-2 classifier makes use of higher precision for the feature extraction and classification; however, it reuses the low-precision results of the level-1 classifier. The process is repeated in an iterative manner until the test sample is classified with a high probability. The proposed approach differs from existing approaches in the sense that the classifier at a certain level is trained using only the training samples that do not contain the samples that were correctly classified in prior levels. The precision at the different levels of feature extraction and classification are the same for both training and test phases. This is expected to lead to higher classification accuracy. The features and classifiers are computed using approximate computing in an incremental manner. Other innovative aspects include: selection of classes of features that require less energy (e.g., time-domain vs. frequency-domain), ranking of these features using techniques such as minimally-redundant maximally-relevant (mRMR) and use of classifiers such as classification and regression tree (CART) or AdaBoost. Approximate computing of features and classifiers in an incremental manner will be investigated to reduce overall energy consumption while maintaining high sensitivity and specificity. Training of the P-Boost classifier and testing the classifier will be based on same precision; thus there is no disconnect between the precision of the classifiers used for training and testing. The proposed "holistic" approach is likely to result in significant savings in energy consumption compared to state-of-the-art machine learning systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

机器学习系统和分类器将成为未来智能设备的一部分。工业互联网（IIOT）和网络物理系统（CPS）将配备实时功能提取和分类，以在某些情况下提供反馈和/或警告信号。智能医疗设备可以分析信号并触发疗法以改善人类健康。安全系统可以分析活动数据并阻止计划的攻击。减少这些智能设备的能耗对于提高便携式应用中的电池寿命至关重要。该建议涉及减少特征提取和分类中能源消耗的技术。更广泛的影响将在展示一种新方法提取和分类的方法，并在没有降低敏感性和特异性的情况下明显降低能源消耗，以及通过实验室和计算经验进行培训和教育研究生和本科生，并通过实验室和计算经验进行相关学科的学生。拟议的框架计算功能并使用简单的级别进行测试数据，并使用简单的1个级别的级别进行测试数据，从而可以使用低确定的精确性。如果分类成功，则该过程将终止。否则将调用2级分类器。 2级分类器利用更高的精度进行特征提取和分类；但是，它可以重复使用Level-1分类器的低精度结果。以迭代方式重复该过程，直到测试样本以高概率分类为止。所提出的方法与现有方法不同，因为在某个级别上的分类器仅使用不包含未正确分类为先前级别的样本的训练样本进行培训。对于训练和测试阶段，不同特征提取和分类水平的精度相同。预计这将导致更高的分类准确性。功能和分类器以增量方式使用近似计算计算。其他创新方面包括：选择更少能量的特征类别（例如，时间域与频域），使用诸如最小冗余最大相关（MRMR）等技术的这些特征的排名以及分类和回归树（CART）或ADABOOST等分类器的使用。将研究特征和分类器的近似计算，以减少总体能耗，同时保持高灵敏度和特异性。培训P-boost分类器和测试分类器将基于相同的精度；因此，用于培训和测试的分类器的精度之间没有断开连接。与最先进的机器学习系统相比，拟议的“整体”方法可能会大量节省能源消耗。该奖项反映了NSF的法定任务，并且使用基金会的智力优点和更广泛的影响评估标准，认为值得通过评估来获得支持。