EAGER: Low-Energy Architectures for Machine Learning

机器学习系统和分类器将成为未来智能设备的一部分。工业物联网（IIOT）和网络物理系统（CPS）将配备实时特征提取和分类，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治疗，以改善人类健康。安全系统可以分析活动数据并阻止有计划的攻击。降低这些智能设备的能耗对于延长便携式应用中的电池寿命至关重要。该提案涉及减少特征提取和分类中的能量消耗的技术。更广泛的影响将是在展示一种新的方法，在不降低灵敏度和特异性的情况下，显著降低能耗的特征提取和分类，沿着培训和教育研究生和本科生在相关学科通过实验室和计算experiences.The建议的框架计算功能和分类的测试数据使用一个简单的1级分类器，利用低精度。如果分类成功，则过程终止。否则调用第2级分类器。第二级分类器利用较高的精度进行特征提取和分类，然而，它重用了第一级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被分类。所提出的方法不同于现有的方法，在这个意义上，在一定程度上的分类器的训练只使用不包含在先前的水平被正确分类的样本的训练样本。对于训练和测试阶段，在不同级别的特征提取和分类的精度是相同的。预计这将导致更高的分类精度。特征和分类器使用近似计算以增量方式计算。其他创新方面包括：选择需要较少能量的特征类别（例如，时域与频域），使用诸如最小冗余最大相关（mRMR）的技术对这些特征进行排序，以及使用诸如分类和回归树（CART）或AdaBoost的分类器。将研究以增量方式近似计算特征和分类器，以降低总体能耗，同时保持高灵敏度和特异性。P-Boost分类器的训练和分类器的测试将基于相同的精度;因此，用于训练和测试的分类器的精度之间没有脱节。与最先进的机器学习系统相比，拟议的“整体”方法可能会显著节省能源消耗。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.