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

人工智能(AI)系统进入了多租户时代，多个深度神经网络(DNN)模型同时执行。这涉及到多个DNN模型的并发部署、计算和交互，增加了计算复杂性并引发了新的挑战：(1)如何实现可扩展和灵活的计算体系结构，以自适应地托管异构和并发的DNN模型？(2)如何满足多租户DNN场景中的计算灵活性要求？(3)如何在这种情况下实现高效的端到端工具链来构建下一代人工智能应用？该项目通过三个研究项目来应对这些挑战：推力1研究了一种新型的内存中处理(PIM)辅助图形处理单元(GPU)架构，该架构具有创新的多租户支持，解决了重要的资源争用和模型交互问题。推力2探索了专门的面向GPU和PIM的调度技术，以增强平台的性能。最后，推力3通过算法优化和代码部署支持，进一步提升了多租户AI应用的开发周期，顺利完成这些推力，可以实现现代AI计算的突破，支持下一代AI应用。拟议的技术有可能加快人工智能的设计和部署，刺激更广泛的人工智能利用。这可以促进具有社会重要性的重要应用领域，包括自动驾驶、虚拟现实沉浸、智能农业和工业基础设施。该项目还将通过将研究成果纳入相关课程，增加女性和其他未被充分代表的群体在计算机领域的参与，并与研究人员、公司和政府机构共享研究成果，从而使学生和社会受益。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。

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.