CAREER: An adaptive framework to accelerate real-time workloads in heterogeneous and reconfigurable environments

职业：一个自适应框架，可在异构和可重新配置的环境中加速实时工作负载

• 批准号: 2046444
• 负责人: Zhenhua Liu
• 金额: $ 53.31万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046444&HistoricalAwards=false
• 关键词: CAREER; adaptive; framework; accelerate; real

Artificial intelligence and machine learning are enabling real-time decisions based on live data for interactive scientific discovery and mission critical applications such as autonomous driving and smart grid. They are increasingly powered by heterogeneous and even reconfigurable accelerators. The reconfigurability and heterogeneity of accelerators, together with stringent performance requirements and complex dependencies in real-time workloads, bring daunting operational challenges. These issues, if left unaddressed, would slow down scientific discovery and waste lots of computing resources and energy. This project will develop a heterogeneity and reconfigurability aware framework to accelerate real-time artificial intelligence and machine learning without hurting other workloads. It will benefit the society by improving the efficiency of costly computing systems, which saves taxpayers' money and better utilize existing investments. Real-time artificial intelligence and machine learning powered by the framework can better serve the society, e.g., accelerating scientific discovery and enabling data-driven control. The project will bring innovative education, outreach and training opportunities for both academic and industrial participants to train the next generation of researchers and practitioners for the society.Today, managing heterogeneous and reconfigurable systems for diverse workloads with high resource utilization and performance guarantee is an extremely challenging task. This project will design and implement an adaptive framework which automatically detects, profiles, and analyzes both workloads and accelerators on the fly. Based on the information, it adaptively reconfigures them to match resource capabilities with workload needs. Global and local optimization will be used to accommodate multiple types of workloads and the configuring, partitioning, placement, scheduling, and execution of models in each workload. The developed framework will provide provable performance even with partial information in unknown environments, which is urgently needed due to the ever increasing system complexity and volatility in workloads. Novel global resource allocation policies will be developed based on optimization techniques in this project to provide performance guarantee such as fairness, strategyproofness, and Pareto efficiency. Throughout the project, a reciprocal methodology is envisioned: the framework accelerates artificial intelligence/machine learning workloads and artificial intelligence/machine learning techniques enable the framework.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.

人工智能和机器学习正在启用基于实时数据的实时决策，以进行交互式科学发现和任务关键应用，例如自动驾驶和智能电网。它们越来越多地由异质甚至可重构的加速器提供动力。加速器的可重构性和异质性，以及在实时工作负载中的严格绩效要求和复杂的依赖性，带来了艰巨的操作挑战。这些问题，如果没有解决，将减慢科学发现并浪费大量计算资源和能源。该项目将开发出异质性和可重新配置的意识框架，以加速实时人工智能和机器学习，而不会损害其他工作量。它将通过提高昂贵的计算系统的效率来使社会受益，从而节省纳税人的资金并更好地利用现有投资。由框架提供支持的实时人工智能和机器学习可以更好地为社会服务，例如，加速科学发现并启用数据驱动的控制。该项目将为学术和工业参与者带来创新的教育，推广和培训机会，以培训社会的下一代研究人员和从业人员。该项目将设计和实施一个自适应框架，该框架会自动检测，配置文件和分析工作负载和加速器。根据信息，它可以自适应地重新配置它们，以将资源功能与工作负载需求匹配。全局和本地优化将用于容纳多种类型的工作负载以及每个工作负载中模型的配置，分区，安排，调度和执行。即使在未知环境中有部分信息，开发的框架也将提供可证明的性能，由于工作负载中的系统复杂性和波动性的增加，因此迫切需要这种信息。新型的全球资源分配策略将基于该项目的优化技术制定，以提供绩效保证，例如公平，防止策略性和帕累托效率。在整个项目中，设想了一种相互的方法：框架加速了人工智能/机器学习工作负载和人工智能/机器学习技术可以实现框架。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力和更广泛影响的评估来通过评估来支持的，这是值得的。

项目成果

Deep Learning-Assisted Online Task Offloading for Latency Minimization in Heterogeneous Mobile Edge

• DOI: 10.1109/tmc.2023.3285882
• 发表时间: 2024-05
• 期刊: IEEE Transactions on Mobile Computing
• 影响因子: 7.9
• 作者: Yu Liu;Yingling Mao;Z. Liu;Yuanyuan Yang

Applied Online Algorithms with Heterogeneous Predictors

• 发表时间: 2023
• 作者: Jessica Maghakian;Russell Lee;M. Hajiesmaili;Jian Li;R. Sitaraman;Zhenhu Liu

Online Container Scheduling for Data-intensive Applications in Serverless Edge Computing

• DOI: 10.1109/infocom53939.2023.10229034
• 发表时间: 2023-05
• 期刊: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
• 作者: Xiaojun Shang;Yingling Mao;Yu Liu;Yaodong Huang;Zhen Liu;Yuanyuan Yang

Joint Task Offloading and Resource Allocation in Heterogeneous Edge Environments

• DOI: 10.1109/infocom53939.2023.10229015
• 发表时间: 2023-05
• 期刊: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
• 作者: Yu Liu;Yingling Mao;Z. Liu;Fan Ye;Yuanyuan Yang

Energy-Aware Online Task Offloading and Resource Allocation for Mobile Edge Computing

• DOI: 10.1109/icdcs57875.2023.00073
• 发表时间: 2023-07
• 期刊: 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS)
• 作者: Yu Liu;Yingling Mao;Xiaojun Shang;Z. Liu;Yuanyuan Yang