V10: Hardware-Assisted NPU Multi-tenancy for Improved Resource Utilization and Fairness
Yuqi Xue, Yiqi Liu, Lifeng Nai, Jian Huang
Abstract
Modern cloud platforms have deployed neural processing units (NPUs) like Google Cloud TPUs to accelerate online machine learning (ML) inference services. To improve the resource utilization of NPUs, they allow multiple ML applications to share the same NPU, and developed both time-multiplexed and preemptive-based sharing mechanisms. However, our study with real-world NPUs discloses that these approaches suffer from surprisingly low utilization, due to the lack of support for fine-grained hardware resource sharing in the NPU. Specifically, its separate systolic array and vector unit cannot be fully utilized at the same time, which requires fundamental hardware assistance for supporting multi-tenancy.