Using Analytical Performance/Power Model and Fine-Grained DVFS to Enhance AI Accelerator Energy Efficiency
Zibo Wang, Yijia Zhang, F. L. Wei, Bingqiang Wang, Yanlin Liu, Zhiheng Hu, Jingyi Zhang, Xiaoxin Xu, Jian He, Xiaoliang Wang, Wanchun Dou, Guihai Chen, Chen Tian
Abstract
Recent advancements in deep learning have significantly increased AI processors' energy consumption, which is becoming a critical factor limiting AI development. Dynamic Voltage and Frequency Scaling (DVFS) stands as a key method in power optimization. However, due to the latency of DVFS control in AI processors, previous works typically apply DVFS control at the granularity of a program's entire duration or sub-phases, rather than at the level of AI operators.
Topics & Concepts
Computer sciencePower (physics)Energy (signal processing)Efficient energy useNuclear engineeringElectrical engineeringEngineeringPhysicsQuantum mechanicsRadiation Effects in ElectronicsParallel Computing and Optimization TechniquesEnergy Harvesting in Wireless Networks