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）系统已进入“多租户”时代，多个深度神经网络（DNN）模型同时执行，这涉及多个DNN模型的并发部署、计算和交互，增加了计算复杂度并引发新的挑战。 ：（1）如何实现可自适应托管异构和并发DNN模型的可扩展且灵活的计算架构？（2）如何满足多租户DNN场景中的计算灵活性要求？在这种背景下，可以实现用于构建下一代人工智能应用程序的端到端工具链吗？该项目通过三个研究重点来应对这些挑战： Thrust 1 研究了一种新颖的内存处理 (PIM) 辅助图形处理单元 (GPU) Thrust 2 探索了面向 GPU 和 PIM 的专用调度技术，以增强平台的性能。最后，Thrust 3 进一步增强了多租户 AI。具有算法优化和代码部署支持的应用程序开发周期。随着这些目标的成功完成，该项目可以在现代人工智能计算方面取得突破，并支持下一代人工智能应用程序，有可能加速人工智能设计和部署，刺激更广泛的人工智能应用。这可以为具有社会重要性的重要应用领域做出贡献，包括自动驾驶、虚拟现实、智能农业和工业基础设施。该项目还将通过整合研究成果使学生受益，从而造福社会。在相关课程中，增加妇女和其他人的参与该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。