CAREER: Rethinking PIM-Assisted GPU Computing for Multi-Tenant Artificial Intelligence

职业：重新思考用于多租户人工智能的 PIM 辅助 GPU 计算

• 批准号: 2239638
• 负责人: Chenchen Liu
• 金额: $ 53.9万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239638&HistoricalAwards=false
• 关键词: CAREER; Rethinking; PIM; Assisted; GPU

Artificial intelligence (AI) systems have entered the “multi-tenant” era, where multiple deep neural network (DNN) models are executed simultaneously. This involves concurrent deployment, computation, and interaction of multiple DNN models, increasing computational complexity and triggering new challenges: (1) How can a scalable and flexible computing architecture be realized that can adaptively host heterogeneous and concurrent DNN models? (2) How can computing flexibility requirements in multi-tenant DNN scenarios be met? (3) How can an efficient, end-to-end toolchain for building next-generation AI applications be realized in this context? This project addresses these challenges through three research thrusts: Thrust 1 investigates a novel processing-in-memory (PIM)-assisted graphics processing unit (GPU) architecture with innovative multi-tenant support, addressing important resource contention and model interaction issues. Thrust 2 explores dedicated GPU- and PIM-oriented scheduling techniques to enhance the platform’s performance. Finally, thrust 3 further enhances the multi-tenant AI application development cycle with algorithm optimization and code deployment support.With the successful completion of these thrusts, this project can achieve breakthroughs in modern AI computing and support the next generation of AI applications. The proposed techniques have the potential to accelerate AI design and deployment, spurring even wider AI utilization. This can contribute to important application areas with societal importance, including autonomous driving, metaverse immersion, smart agriculture, and industrial infrastructure. This project will also benefit students --and by consequence, society-- by incorporating research results within relevant courses, increasing the participation of women and other underrepresented groups in computing, and sharing research results with researchers, companies, and government agencies.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（AI）系统进入了“多租户”时代，在该时代中，简单地执行了多个深神经网络（DNN）模型。这涉及多个DNN模型的同时部署，计算和相互作用，提高了计算复杂性并触发新的挑战：（1）如何实现可以自适应宿主的异质和同意DNN模型的可扩展和灵活计算体系结构？ （2）如何满足多租户DNN场景中的灵活性要求？ （3）在这种情况下，如何实现用于构建下一代AI应用程序的高效的端到端工具链？该项目通过三个研究推力来解决这些挑战：Thrust 1研究了一种新颖的内存处理（PIM）辅助图形处理单元（GPU）架构，并具有创新的多租户支持，以解决重要的资源争夺和模型交互问题。推力2探讨了专用的GPU和面向PIM的调度技术，以提高平台的性能。最后，推力3进一步增强了通过算法优化和代码部署支持的多租户AI应用程序开发周期。在成功完成这些推力的过程中，该项目可以在现代AI计算中取得突破，并支持下一代AI应用程序。所提出的技术有可能加速AI设计和部署，从而激发了更广泛的AI利用率。这可以促进具有社会重要性的重要应用领域，包括自主驾驶，元融入，智能农业和工业基础设施。该项目还将通过增加相关课程中的研究成果，增加妇女和其他代表性不足的群体的参与，并与研究人员，公司和政府机构共享研究结果。这项奖项反映了NSF的法定任务，并通过基金会的智力评估评估，该奖项是对基金会的评估，该奖项将使研究成果受益。