SHF: Small: Synthesis of Complex Deep Neural Networks on Distributed Resource-Constrained Devices

SHF：小型：分布式资源受限设备上复杂深度神经网络的综合

基本信息

批准号：
2006394
负责人：
Azadeh Davoodi
金额：
$ 49.99万
依托单位：
University of Wisconsin-Madison
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006394&HistoricalAwards=false
关键词：
SHF Small Synthesis Complex Deep

项目摘要

Deep neural network (DNN) machine learning algorithms have become significantly complex in order to address challenges in modern Information Technology applications such as smart city, autonomous driving, pervasive health care, among others. This complexity necessitates execution on high-end computing platforms which typically reside in a cloud infrastructure. Such applications rely on simple Internet of Things (IoT) devices, such as sensors, to gather data, which typically transfer their data via network to the cloud infrastructure for DNN processing and then receive back an inference. However, such an approach is not scalable to the billions of IoT devices that are projected in the near future. In addition, it cannot work when the network infrastructure is unavailable, and may not allow customization to learn from individual needs or for specific environments. In this project, new techniques will be investigated to directly deploy complex DNNs onto simple IoT devices which work in parallel, with the primary goal to perform an inference task as fast as possible. Technology developed in this project will enable rapid development of smart IoT devices, which can serve as fundamental building blocks of next generation smart services. The research outcomes will be disseminated through intellectual property filing, publication, lectures, and software distribution. They will also be integrated as new curriculum materials into existing courses. Graduate and undergraduate students will be involved in the research activities, with an effort to recruit from women and underrepresented minorities.Specific research activities include: (1) exploring a new methodology to synthesize a complex DNN as a collection of smaller DNNs which are mapped to distributed IoT devices; (2) developing "class-aware" structure simplification techniques to aggressively prune any of the small DNNs when the goal is to cover a subset of the label space on a single IoT device and generate a "don't know" signal for the rest; (3) enhancing the inference accuracy via performing low-cost customizations which arise from usage and environmental factors, when implemented locally on each IoT device.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.

为了应对智慧城市、自动驾驶、普及医疗保健等现代信息技术应用中的挑战，深度神经网络 (DNN) 机器学习算法已经变得非常复杂。这种复杂性需要在通常驻留在云基础设施中的高端计算平台上执行。此类应用程序依靠简单的物联网 (IoT) 设备（例如传感器）来收集数据，这些设备通常通过网络将数据传输到云基础设施进行 DNN 处理，然后接收推论。然而，这种方法无法扩展到预计在不久的将来出现的数十亿个物联网设备。此外，当网络基础设施不可用时，它就无法工作，并且可能不允许根据个人需求或针对特定环境进行定制。在该项目中，将研究新技术，将复杂的 DNN 直接部署到并行工作的简单物联网设备上，主要目标是尽快执行推理任务。该项目开发的技术将促进智能物联网设备的快速开发，这些设备可以作为下一代智能服务的基本构建模块。研究成果将通过知识产权申请、出版、讲座和软件分发等方式传播。它们还将作为新课程材料整合到现有课程中。研究生和本科生将参与研究活动，努力从女性和代表性不足的少数群体中招募人才。具体研究活动包括：（1）探索一种新的方法来合成复杂的 DNN，将其映射为较小的 DNN 的集合。分布式物联网设备； (2) 当目标是覆盖单个物联网设备上标签空间的子集并为其余部分生成“不知道”信号时，开发“类感知”结构简化技术来积极修剪任何小型 DNN ; (3) 在每个物联网设备上本地实施时，通过执行因使用和环境因素而产生的低成本定制来提高推理准确性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和评估进行评估，被认为值得支持。更广泛的影响审查标准。