[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
research-article

PredictNcool: Leakage Aware Thermal Management for 3D Memories Using a Lightweight Temperature Predictor

Published: 08 October 2019 Publication History

Abstract

Recent research on mitigating thermal problems in 3D memories has covered reactive strategies that reduce memory power consumption, and thereby, performance, when the memory temperature reaches the maximum operating limit. Such techniques could benefit from temperature prediction and avoid unnecessary invocations and state transitions of the thermal management strategy. We develop an accurate steady state temperature predictor for thermal management of 3D memories. We utilize the symmetries in the floorplan, along with other design insights, to reduce the predictor’s model parameters, making it lightweight and suitable for runtime thermal management. Using the temperature prediction, we introduce PredictNcool, a proactive thermal management strategy to reduce application runtime and memory energy. We compare PredictNcool with two recent thermal management strategies and our experiments show that the proposed optimization results in performance improvements of 28% and 5%, and memory subsystem energy reductions of 38% and 12% (on average).

References

[1]
T. Adegbija and A. Gordon-Ross. 2018. TaPT: Temperature-aware dynamic cache optimization for embedded systems. Computers (2018).
[2]
R. Ayoub, K. R. Indukuri, and T. S. Rosing. 2010. Energy efficient proactive thermal management in memory subsystem. In ISLPED.
[3]
R. Ayoub and A. Orailoglu. 2010. Performance and energy efficient cache migration approach for thermal management in embedded systems. In GLSVLSI.
[4]
P. Bogdan, P. P. Pande, H. Amrouch, M. Shafique, and J. Henkel. 2016. Power and thermal management in massive multicore chips: Theoretical foundation meets architectural innovation and resource allocation. In CASES.
[5]
D. Brooks and M. Martonosi. 2001. Dynamic thermal management for high-performance microprocessors. In HPCA.
[6]
D. Calvo, P. González, L. Díaz, H. Posadas, P. Sánchez, E. Villar, A. Acquaviva, and E. Macii. 2011. A multi-processing systems-on-chip native simulation framework for power and thermal-aware design. JOLPE (2011).
[7]
T. E. Carlson, W. Heirman, S. Eyerman, I. Hur, and L. Eeckhout. 2014. An evaluation of high-level mechanistic core models. TACO (2014).
[8]
K. Chen et al. 2012. CACTI-3DD: Architecture-level modeling for 3D die-stacked DRAM main memory. In DATE.
[9]
R. Cochran and S. Reda. 2013. Thermal prediction and adaptive control through workload phase detection. TODAES (2013).
[10]
A. K. Coskun, T. S. Rosing, and K. C Gross. 2008. Proactive temperature management in MPSoCs. In ISLPED.
[11]
D. Cuesta, J. Ayala, J. Hidalgo, M. Poncino, A. Acquaviva, and E. Macii. 2010. Thermal-aware floorplanning exploration for 3D multi-core architectures. In GLSVLSI.
[12]
K. Dev, A. N. Nowroz, and S. Reda. 2013. Power mapping and modeling of multi-core processors. In ISLPED.
[13]
A. Fourmigue, G. Beltrame, and G. Nicolescu. 2014. Efficient transient thermal simulation of 3D ICs with liquid-cooling and through silicon vias. In DATE.
[14]
M. H. Hajkazemi et al. 2017. Heterogeneous HMC+DDRx memory management for performance-temperature tradeoffs. JETCS (Sept. 2017).
[15]
F. Hameed, M. A. A. Faruque, and J. Henkel. 2011. Dynamic thermal management in 3D multi-core architecture through run-time adaptation. In DATE.
[16]
J. L. Henning. 2006. SPEC CPU2006 benchmark descriptions. ACM SIGARCH Computer Architecture News (2006).
[17]
H. Homayoun, A. Gupta, A. Veidenbaum, A. Sasan, F. Kurdahi, and N. Dutt. 2010. RELOCATE: Register file local access pattern redistribution mechanism for power and thermal management in out-of-order embedded processor. In HiPEAC.
[18]
H. Huang, K. G. Shin, C. Lefurgy, and T. Keller. 2005. Improving energy efficiency by making DRAM less randomly accessed. In ISLPED.
[19]
J. Jeddeloh and B. Keeth. 2012. Hybrid memory cube new DRAM architecture increases density and performance. In VLSIT.
[20]
D. Juan et al. 2014. Statistical peak temperature prediction and thermal yield improvement for 3D chip multiprocessors. TODAES (2014).
[21]
D. Juan, H. Zhou, D. Marculescu, and X. Li. 2012. A learning-based autoregressive model for fast transient thermal analysis of chip-multiprocessors. In ASPDAC.
[22]
P. Kumar and D. Atienza. 2010. Neural network based on-chip thermal simulator. In ISCAS.
[23]
D. Lee, S. Das, J. R. Doppa, P. P. Pande, and K. Chakrabarty. 2018. Performance and thermal tradeoffs for energy-efficient monolithic 3D network-on-chip. TODAES (2018).
[24]
C. H. Liao, C. H. P. Wen, and K. Chakrabarty. 2015. An online thermal-constrained task scheduler for 3D multi-core processors. In DATE’15.
[25]
W. Liu, L. Yang, W. Jiang, L. Feng, N. Guan, W. Zhang, and N. Dutt. 2018. Thermal-aware task mapping on dynamically reconfigurable network-on-chip based multiprocessor system-on-chip. TC (2018).
[26]
W. Lo et al. 2016. Thermal-aware dynamic page allocation policy by future access patterns for Hybrid Memory Cube (HMC). In DATE.
[27]
G. L. Loi et al. 2006. A thermally-aware performance analysis of vertically integrated (3D) processor-memory hierarchy. In DAC.
[28]
Y. Lu et al. 2016. Rank-aware dynamic migrations and adaptive demotions for DRAM power management. TC (2016).
[29]
J. Meng et al. 2012. Optimizing energy efficiency of 3-D multicore systems with stacked DRAM under power and thermal constraints. In DAC.
[30]
F. Pedregosa et al. 2011. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12 (2011), 2825--2830.
[31]
L. Siddhu and P. R. Panda. 2019. FastCool: Leakage aware dynamic thermal management of 3D memories. In DATE.
[32]
G. Singla, G. Kaur, A. K. Unver, and U. Y. Ogras. 2015. Predictive dynamic thermal and power management for heterogeneous mobile platforms. In DATE.
[33]
F. Sironi, M. Maggio, R. Cattaneo, G. F. D. Nero, D. Sciuto, and M. D. Santambrogio. 2013. ThermOS: System support for dynamic thermal management of chip multi-processors. In PACT.
[34]
I. G. Thakkar, S. Pasricha, et al. 2018. LIBRA: Thermal and process variation aware reliability management in photonic networks-on-chip. TMSCS (2018).
[35]
S. Xydis, G. Palermo, and C. Silvano. 2013. Thermal-aware datapath merging for coarse-grained reconfigurable processors. In DATE.
[36]
Y. Ye, Z. Wang, P. Yang, J. Xu, X. Wu, X. Wang, M. Nikdast, Z. Wang, and L. H. K. Duong. 2014. System-level modeling and analysis of thermal effects in WDM-based optical networks-on-chip. TCAD (2014).
[37]
M. Zapater et al. 2013. Leakage and temperature aware server control for improving energy efficiency in data centers. In DATE.
[38]
K. Zhang, A. Guliani, S. Ogrenci-Memik, G. Memik, K. Yoshii, R. Sankaran, and P. Beckman. 2018. Machine learning-based temperature prediction for runtime thermal management across system components. TPDS (2018).
[39]
R. Zhang, M. R. Stan, and K. Skadron. 2015. HotSpot 6.0: Validation, acceleration and extension. (2015).
[40]
Z. Zhao, A. Gerstlauer, and L. K. John. 2017. Source-level performance, energy, reliability, power and thermal (PERPT) simulation. TCAD (2017).
[41]
J. Zheng, N. Wu, L. Zhou, Y. Ye, and K. Sun. 2016. DFSB-based thermal management scheme for 3D NoC-bus architectures. TVLSI (2016).

Cited By

View all
  • (2024)NeuroTAP: Thermal and Memory Access Pattern-Aware Data Mapping on 3D DRAM for Maximizing DNN PerformanceACM Transactions on Embedded Computing Systems10.1145/367717823:6(1-30)Online publication date: 11-Sep-2024
  • (2024)An Evaluation Framework for Dynamic Thermal Management Strategies in 3D MultiProcessor System-on-Chip Co-DesignIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.345941435:11(2161-2176)Online publication date: 1-Nov-2024
  • (2023)NeuroCool: Dynamic Thermal Management of 3D DRAM for Deep Neural Networks through Customized PrefetchingACM Transactions on Design Automation of Electronic Systems10.1145/363001229:1(1-35)Online publication date: 18-Dec-2023
  • Show More Cited By

Index Terms

  1. PredictNcool: Leakage Aware Thermal Management for 3D Memories Using a Lightweight Temperature Predictor

        Recommendations

        Comments

        Please enable JavaScript to view thecomments powered by Disqus.

        Information & Contributors

        Information

        Published In

        cover image ACM Transactions on Embedded Computing Systems
        ACM Transactions on Embedded Computing Systems  Volume 18, Issue 5s
        Special Issue ESWEEK 2019, CASES 2019, CODES+ISSS 2019 and EMSOFT 2019
        October 2019
        1423 pages
        ISSN:1539-9087
        EISSN:1558-3465
        DOI:10.1145/3365919
        Issue’s Table of Contents
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Journal Family

        Publication History

        Published: 08 October 2019
        Accepted: 01 July 2019
        Revised: 01 June 2019
        Received: 01 April 2019
        Published in TECS Volume 18, Issue 5s

        Permissions

        Request permissions for this article.

        Check for updates

        Author Tags

        1. 3D memories
        2. energy efficiency
        3. thermal management

        Qualifiers

        • Research-article
        • Research
        • Refereed

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • Downloads (Last 12 months)42
        • Downloads (Last 6 weeks)9
        Reflects downloads up to 14 Dec 2024

        Other Metrics

        Citations

        Cited By

        View all
        • (2024)NeuroTAP: Thermal and Memory Access Pattern-Aware Data Mapping on 3D DRAM for Maximizing DNN PerformanceACM Transactions on Embedded Computing Systems10.1145/367717823:6(1-30)Online publication date: 11-Sep-2024
        • (2024)An Evaluation Framework for Dynamic Thermal Management Strategies in 3D MultiProcessor System-on-Chip Co-DesignIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.345941435:11(2161-2176)Online publication date: 1-Nov-2024
        • (2023)NeuroCool: Dynamic Thermal Management of 3D DRAM for Deep Neural Networks through Customized PrefetchingACM Transactions on Design Automation of Electronic Systems10.1145/363001229:1(1-35)Online publication date: 18-Dec-2023
        • (2023)Dynamic Thermal Management of 3D Memory through Rotating Low Power States and Partial Channel ClosureACM Transactions on Embedded Computing Systems10.1145/362458122:6(1-27)Online publication date: 9-Nov-2023
        • (2022)CoMeT: An Integrated Interval Thermal Simulation Toolchain for 2D, 2.5D, and 3D Processor-Memory SystemsACM Transactions on Architecture and Code Optimization10.1145/353218519:3(1-25)Online publication date: 22-Aug-2022
        • (2022)NeuroMap: Efficient Task Mapping of Deep Neural Networks for Dynamic Thermal Management in High-Bandwidth MemoryIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.319769841:11(3602-3613)Online publication date: 1-Nov-2022
        • (2021)Variability-Aware Thermal Simulation using CNNs2021 34th International Conference on VLSI Design and 2021 20th International Conference on Embedded Systems (VLSID)10.1109/VLSID51830.2021.00016(65-70)Online publication date: Feb-2021
        • (2020)Leakage-Aware Dynamic Thermal Management of 3D MemoriesACM Transactions on Design Automation of Electronic Systems10.1145/341946826:2(1-31)Online publication date: 23-Oct-2020

        View Options

        Login options

        Full Access

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format.

        HTML Format

        Media

        Figures

        Other

        Tables

        Share

        Share

        Share this Publication link

        Share on social media