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

人工智能和机器学习使基于实时数据的实时决策成为可能，用于交互式科学发现和任务关键型应用，如自动驾驶和智能电网。它们越来越多地由不同的、甚至是可重新配置的加速器提供动力。加速器的可重配性和异构性，再加上严格的性能要求和实时工作负载中的复杂依赖关系，带来了令人望而生畏的运营挑战。这些问题如果不加以解决，将减缓科学发现的速度，浪费大量的计算资源和能源。该项目将开发一个异构性和可重构性感知框架，以在不损害其他工作负载的情况下加速实时人工智能和机器学习。它将通过提高昂贵的计算系统的效率来造福社会，从而节省纳税人的钱并更好地利用现有投资。由该框架驱动的实时人工智能和机器学习可以更好地服务于社会，例如，加速科学发现和启用数据驱动控制。该项目将为学术界和产业界的参与者带来创新的教育、推广和培训机会，为社会培养下一代研究人员和实践者。今天，在资源利用率高和性能保证的情况下，为不同的工作负载管理异构和可重构的系统是一项极具挑战性的任务。这个项目将设计和实现一个自适应框架，它可以动态地自动检测、分析和分析工作负载和加速器。根据这些信息，它会自适应地重新配置它们，以使资源能力与工作负载需求相匹配。将使用全局和局部优化来适应多种类型的工作负载，以及在每个工作负载中配置、分区、放置、调度和执行模型。开发的框架将在未知环境中提供可证明的性能，即使使用部分信息，这是由于工作负载中的系统复杂性和波动性不断增加而迫切需要的。该项目将基于优化技术开发新的全局资源分配策略，以提供公平性、无策略性和帕累托效率等性能保证。在整个项目中，设想了一种互惠的方法：该框架加速了人工智能/机器学习的工作量，人工智能/机器学习技术使该框架成为可能。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更广泛的影响审查标准进行评估，被认为值得支持。

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.