RTML: Large: Real-Time Autonomic Decision Making on Sparsity-Aware Accelerated Hardware via Online Machine Learning and Approximation

RTML：大型：通过在线机器学习和近似在稀疏感知加速硬件上进行实时自主决策

• 批准号: 1937403
• 负责人: Dario Pompili
• 金额: $ 140万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1937403&HistoricalAwards=false
• 关键词: RTML; Large; Real; Time; Autonomic

Real-time smart and autonomic decision making involves two major stages, sensing (of sensor data and then transformation into actionable knowledge) and planning (taking decisions using this knowledge). These two stages happen in both internal and external operations of an Intelligent Physical System (IPS). In case of internal operations, sensing refers to reading data from on-board sensors and planning refers to smart execution of the firmware running on the IPS. In case of external operations, sensing refers to sensing data from externally-mounted sensors and planning refers to executing the software that constitutes an application. In the sensing stage, an IPS should be able to cope with different forms of uncertainty, especially data and model uncertainties. The goal of this research project is to achieve the objectives of online autonomic decision making on sparsity-aware accelerated hardware via Real-Time Machine Learning (RTML) and approximation for a group of IPSs such as drones performing data collection and/or multi-object tracking/classification and operating in a highly dynamic environment that is difficult to model. Remarkably, the techniques adopted in this project generalize well as they can be applied to a variety of IPS domains including natural calamities, man-made disasters, and terrorist attacks. The drone-based distributed multi-object tracking/classification will enable stakeholders such as citizens, government bodies, rescue agencies, and industries to comprehend the extent of damage, and to develop more effective mitigation policies. The research will also train students including minority and underrepresented students in the field.There are three specific tasks in this project. In Task 1, a real-time decision-making approach will be proposed via online deep reinforcement learning with inherent distributed training capability; temporal and spatial correlation in streaming video will then be exploited towards real-time multi-object tracking/detection. In Task 2, novel hardware architectures will be designed to support sparse Convolution Neural Networks (CNN). Considering the dual benefits of sparsity on both lower computational and space complexity for Deep Neural Network (DNN) models, a sparsity-aware CNN accelerator can achieve significant hardware performance improvements in term of latency, throughput, and energy efficiency over non-sparsity-aware techniques. Finally, in Task 3, hardware-aware software engineering solutions will be studied for accelerated execution. The idea of leveraging compiler optimization and the underlying hardware features in combination will be investigated in order to optimize execution performance; then, data-driven modeling techniques will be presented to replace the time-consuming segments of the ML software packages with their equivalent data-driven models, namely micro-neural networks. Once these three research tasks are validated individually via principled experimentation in terms of their stated goals, they will be integrated into a unified framework, which will be thoroughly studied via multiple trials on complementary field scenarios. The project will also collaborate with a synergistic DARPA program for related hardware development.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.

实时智能和自主性决策涉及两个主要阶段，即传感（传感器数据，然后转换为可行的知识）和计划（使用此知识做出决策）。这两个阶段发生在智能物理系统（IPS）的内部和外部操作中。如果进行内部操作，感应是指从板载传感器中读取数据，并计划是指在IPS上运行的固件的智能执行。在外部操作的情况下，传感是指从外部安装的传感器中传感数据，并计划是指执行构成应用程序的软件。在感应阶段，IPS应该能够应对不同形式的不确定性，尤其是数据和模型不确定性。该研究项目的目的是通过实时机器学习（RTML）（RTML）实现在线自主神经决策的目标，以及一组IPS的近似值，例如无人机，例如执行数据收集和/或多动物对象跟踪/分类和在很难模拟模型的高度动态环境中运行的IPS。值得注意的是，该项目采用的技术可以很好地应用于各种IPS领域，包括自然灾害，人造灾难和恐怖袭击。基于无人机的分布式多对象跟踪/分类将使利益相关者（例如公民，政府机构，救援机构和行业）能够理解损害的程度，并制定更有效的缓解政策。这项研究还将培训包括该领域的少数民族和代表性不足的学生在内的学生。该项目有三个特定的任务。在任务1中，将通过具有固有的分布式培训能力的在线深层强化学习提出一种实时决策方法；然后，流媒体视频中的时间和空间相关性将被利用为实时多对象跟踪/检测。在任务2中，新颖的硬件体系结构将设计为支持稀疏卷积神经网络（CNN）。考虑到深度神经网络（DNN）模型对较低的计算和空间复杂性的双重好处，稀疏感知的CNN加速器可以在延迟，吞吐量和能源效率方面取得重大的硬件性能改善，而不是非sparsity-partersity-Area Area Area Area Area Area Area Area Area。最后，在任务3中，将研究硬件感知软件工程解决方案以加速执行。为了优化执行性能，将研究利用编译器优化和基础硬件功能的想法；然后，将介绍数据驱动的建模技术，以替换ML软件包的耗时段，并替换其等效数据驱动的模型，即微神经网络。一旦这三个研究任务通过其既定目标进行了有原则的实验验证，它们将被整合到一个统一的框架中，该框架将通过有关互补现场场景的多次试验进行彻底研究。该项目还将与一个协同的DARPA计划合作，以用于相关硬件开发。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来获得支持。