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.

实时智能和自主决策涉及两个主要阶段，感知（传感器数据，然后转换为可操作的知识）和规划（使用这些知识做出决策）。这两个阶段发生在智能物理系统（IPS）的内部和外部操作中。在内部操作的情况下，感测是指从板载传感器阅读数据，规划是指智能执行IPS上运行的固件。在外部操作的情况下，感测是指从外部安装的传感器感测数据，规划是指执行构成应用程序的软件。在传感阶段，IPS应能够科普不同形式的不确定性，特别是数据和模型的不确定性。该研究项目的目标是通过实时机器学习（RTML）和近似一组IPS（如执行数据收集和/或多目标跟踪/分类的无人机）实现在线自主决策的目标，并在难以建模的高度动态环境中运行。值得注意的是，该项目中采用的技术具有很好的通用性，因为它们可以应用于各种IPS领域，包括自然灾害，人为灾害和恐怖袭击。基于无人机的分布式多目标跟踪/分类将使公民、政府机构、救援机构和行业等利益相关者能够了解损害程度，并制定更有效的减灾政策。本研究也将训练学生，包括少数民族和代表性不足的学生在该领域。在任务1中，将通过具有固有分布式训练能力的在线深度强化学习提出一种实时决策方法;然后将利用流视频中的时间和空间相关性进行实时多目标跟踪/检测。在任务2中，将设计新的硬件架构来支持稀疏卷积神经网络（CNN）。考虑到稀疏性在深度神经网络（DNN）模型的较低计算和空间复杂性方面的双重好处，稀疏感知CNN加速器可以在延迟，吞吐量和能源效率方面实现显着的硬件性能改进。最后，在任务3中，将研究硬件感知的软件工程解决方案以加速执行。将研究利用编译器优化和底层硬件功能相结合的想法，以优化执行性能;然后，将提出数据驱动的建模技术，用等效的数据驱动模型（即微神经网络）取代ML软件包中耗时的部分。一旦这三项研究任务通过原则性实验根据其既定目标进行单独验证，它们将被整合到一个统一的框架中，并将通过对互补现场场景的多次试验进行彻底研究。该项目还将与DARPA的一个协同项目合作，进行相关的硬件开发。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。