FlexWatts: A Power- and Workload-Aware Hybrid Power Delivery Network for Energy-Efficient Microprocessors
Jawad Haj-Yahya, Mohammed Alser, Jeremie S. Kim, Lois Orosa, Efraim Rotem, Avi Mendelson, Anupam Chattopadhyay, Onur Mutlu
Abstract
Modern client processors typically use one of three commonly-used power delivery network (PDN) architectures: 1) motherboard voltage regulators (MBVR), 2) integrated voltage regulators (IVR), and 3) low dropout voltage regulators (LDO). We observe that the energy-efficiency of each of these PDNs varies with the processor power (e.g, thermal design power (TDP) and dynamic power-state) and workload characteristics (e.g., work-load type and computational intensity). This leads to energy-inefficiency and performance loss, as modern client processors operate across a wide spectrum of power consumption and execute a wide variety of workloads. To address this inefficiency, we propose FlexWatts, a hybrid adaptive PDN for modern client processors whose goal is to provide high energy-efficiency across the processor's wide range of power consumption and workloads. FlexWatts provides high energy-efficiency by intelligently and dynamically allocating PDNs to processor domains depending on the processor's power consumption and workload. FlexWatts is based on three key ideas. First, FlexWatts combines IVRs and LDOs in a novel way to share multiple on-chip and off-chip resources and thus reduce cost, as well as board and die area overheads. This hybrid PDN is allocated for processor domains with a wide power consumption range (e.g., CPU cores and graphics engines) and it dynamically switches between two modes: IVR-Mode and LDO-Mode, depending on the power consumption. Second, for all other processor domains (that have a low and narrow power range, e.g., the IO domain), FlexWatts statically allocates off-chip VRs, which have high energy-efficiency for low and narrow power ranges. Third, FlexWatts introduces a novel prediction algorithm that automatically switches the hybrid PDN to the mode (IVR-Mode or LDO-Mode) that is the most beneficial based on processor power consumption and workload characteristics. To evaluate the tradeoffs of PDNs, we develop and open-source PDNspot, the first validated architectural PDN model that enables quantitative analysis of PDN metrics. Using PDNspot, we evaluate FlexWatts on a wide variety of SPEC CPU2006, graphics (3DMark06), and battery life (e.g., video playback) workloads against IVR, the state-of-the-art PDN in modern client processors. For a 4 W thermal design power (TDP) processor, FlexWatts improves the average performance of the SPEC CPU2006 and 3DMark06 workloads by 22% and 25%, respectively. For battery life workloads, FlexWatts reduces the average power consumption of video playback by 11% across all tested TDPs (4W-50W). FlexWatts has comparable cost and area overhead to IVR. We conclude that FlexWatts provides high energy-efficiency across a modern client processor's wide range of power consumption and wide variety of workloads, with minimal overhead.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 110ae48d-e0b0-4250-8aeb-591324c5946aCited by top-tier papers4
- AgileWatts: An Energy-Efficient CPU Core Idle-State Architecture for Latency-Sensitive Server ApplicationsJawad Haj-Yahya, Haris Volos, Davide B. Bartolini, Georgia Antoniou et al.MICRO 2022 · 22 citations
- IChannels: Exploiting Current Management Mechanisms to Create Covert Channels in Modern ProcessorsJawad Haj-Yahya, Lois Orosa, Jeremie S. Kim, Juan Gómez-Luna et al.ISCA 2021 · 19 citations
- AgilePkgC: An Agile System Idle State Architecture for Energy Proportional Datacenter ServersGeorgia Antoniou, Haris Volos, Davide B. Bartolini, Tom Rollet et al.MICRO 2022 · 6 citations
- BurstLink: Techniques for Energy-Efficient Video Display for Conventional and Virtual Reality SystemsJawad Haj-Yahya, Jisung Park, Rahul Bera, Juan Gómez-Luna et al.MICRO 2021 · 5 citations
Builds on1
Related papers
- CDAR-DRAM: An In-situ Charge Detection and Adaptive Data Restoration DRAM Architecture for Performance and Energy Efficiency ImprovementChuxiong Lin, Weifeng He, Yanan Sun, Zhigang Mao et al.DAC 2021 · 6 citations
- PowerLens: An Adaptive DVFS Framework for Optimizing Energy Efficiency in Deep Neural NetworksJiawei Geng, Zongwei Zhu, Weihong Liu, Xuehai Zhou et al.DAC 2024 · 10 citations
- A Cross-Layer Power and Timing Evaluation Method for Wide Voltage ScalingWenjie Fu, Leilei Jin, Ming Ling, Yu Zheng et al.DAC 2020 · 5 citations
- Secondary-Power-Cell-Aware Detailed Placement in Multiple Power Domain DesignsYu-Wei Chang, Shao-Yun Fang, Kai-Chuan Yang, Min-Ching LinDAC 2025
- Truly Pre-Routing Timing Prediction via Considering Power Delivery NetworkYuyang Ye, Mingwei He, Lizheng Ren, Jianwang Zhai et al.DAC 2025
