Document Open Access Logo

Towards Multi-Objective Dynamic SPM Allocation

Authors Shashank Jadhav , Heiko Falk

PDF
Thumbnail PDF

File

OASIcs.WCET.2023.6.pdf
  • Filesize: 0.64 MB
  • 12 pages

Document Identifiers

Author Details

Shashank Jadhav
  • Hamburg University of Technology, Germany
Heiko Falk
  • Hamburg University of Technology, Germany

Funding

This work is part of a project that received funding from NXP Semiconductors.

Cite AsGet BibTex

Shashank Jadhav and Heiko Falk. Towards Multi-Objective Dynamic SPM Allocation. In 21th International Workshop on Worst-Case Execution Time Analysis (WCET 2023). Open Access Series in Informatics (OASIcs), Volume 114, pp. 6:1-6:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/OASIcs.WCET.2023.6

Abstract

Most real-time embedded systems are required to fulfill timing constraints while adhering to a limited energy budget. Small ScratchPad Memory (SPM) poses a common hardware constraint on embedded systems. Static SPM allocation techniques are limited by the SPM’s stringent size constraint, which is why this paper proposes a Dynamic SPM Allocation (DSA) model at the compiler level for the dynamic allocation of a program to SPM during runtime. To minimize Worst-Case Execution Time (WCET) and energy objectives, we propose a multi-objective DSA-based optimization. Static SPM allocations might inherently use SPM sub-optimally, while all proposed DSA optimizations are only single-objective. Therefore, this paper is the first step towards a DSA that trades WCET and energy objectives simultaneously. Even with extra DSA overheads, our approach provides better quality solutions than the state-of-the-art multi-objective static SPM allocation and ILP-based single-objective DSA approach.

Subject Classification

ACM Subject Classification
  • Computer systems organization → Real-time systems
  • Software and its engineering → Compilers
  • Mathematics of computing → Discrete mathematics
Keywords
  • Multi-objective optimization
  • Embedded systems
  • Compilers
  • Dynamic SPM allocation
  • Metaheuristic algorithms

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. AbsInt Angewandte Informatik, GmbH. aiT Worst-Case Execution Time Analyzers, 2021. Google Scholar
  2. Andrew W Appel. Modern compiler implementation in C. Cambridge university press, 2004. Google Scholar
  3. ARM Limited. ARM Compiler armasm User Guide Version 5.06, 2010-2016. Google Scholar
  4. Sanghamitra Bandyopadhyay and Arpan Mukherjee. An algorithm for many-objective optimization with reduced objective computations: A study in differential evolution. IEEE Transactions on Evolutionary Computation, 19(3):400-413, 2014. Google Scholar
  5. Jean-Francois Deverge and Isabelle Puaut. WCET-Directed Dynamic Scratchpad Memory Allocation of Data. In ECRTS, pages 179-190, 2007. Google Scholar
  6. Heiko Falk and Paul Lokuciejewski. A Compiler Framework for the Reduction of Worst-Case Execution Times. Real-Time Systems, 46(2):251-298, 2010. Google Scholar
  7. Poletti Francesco, Paul Marchal, David Atienza, et al. An integrated hardware/software approach for run-time scratchpad management. In DAC, pages 238-243, 2004. Google Scholar
  8. Michael R Garey, Ronald L Graham, and Jeffrey D Ullman. Worst-case analysis of memory allocation algorithms. In Proceedings of the fourth annual ACM symposium on Theory of computing, pages 143-150, 1972. Google Scholar
  9. Chi-Keong Goh and Kay Chen Tan. A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Transactions on Evolutionary Computation, 13(1):103-127, 2008. Google Scholar
  10. Kenneth Hoste and Lieven Eeckhout. Cole: compiler optimization level exploration. In Proceedings of the 6th annual IEEE/ACM International Symposium on Code Generation and Optimization, pages 165-174. ACM, 2008. Google Scholar
  11. Shashank Jadhav and Heiko Falk. Multi-objective optimization for the compiler of real-time systems based on flower pollination algorithm. In Proceedings of the 22nd International Workshop on Software and Compilers for Embedded Systems, pages 45-48, 2019. Google Scholar
  12. Shashank Jadhav and Heiko Falk. Approximating wcet and energy consumption for fast multi-objective memory allocation. In Proceedings of the 30th International Conference on Real-Time Networks and Systems, pages 162-172, 2022. Google Scholar
  13. Mahmut Kandemir, J Ramanujam, Mary Jane Irwin, et al. Dynamic management of scratch-pad memory space. In DAC, pages 690-695, 2001. Google Scholar
  14. Yooseong Kim, David Broman, and Aviral Shrivastava. WCET-Aware Function-Level Dynamic Code Management on Scratchpad Memory. ACM TECS, 16(4):1-26, 2017. Google Scholar
  15. Yu Liu and Wei Zhang. Scratchpad memory architectures and allocation algorithms for hard real-time multicore processors. KIISE JCSE, 9(2):51-72, 2015. Google Scholar
  16. Paul Lokuciejewski, Sascha Plazar, Heiko Falk, Peter Marwedel, and Lothar Thiele. Approximating Pareto optimal compiler optimization sequences - a trade-off between WCET, ACET and code size. Software: Practice and Experience, 41(21):1437-1458, 2011. DOI URL: http://dx.doi.org/10.1002/spe.1079.
  17. Kateryna Muts and Heiko Falk. Multi-Criteria Function Inlining for Hard Real-Time Systems. In RTNS, pages 56-66, 2020. DOI URL: http://dx.doi.org/10.1145/3394810.3394819.
  18. Jason A Poovey, Thomas M Conte, Markus Levy, and Shay Gal-On. A benchmark characterization of the eembc benchmark suite. IEEE micro, 29(5):18-29, 2009. Google Scholar
  19. Robert Pyka, Christoph Faßbach, Manish Verma, et al. Operating System Integrated Energy Aware Scratchpad Allocation Strategies for Multiprocess Applications. In SCOPES, 2007. Google Scholar
  20. Meikang Qiu, Zhi Chen, Zhong Ming, et al. Energy-Aware Data Allocation With Hybrid Memory for Mobile Cloud Systems. IEEE ISJ, 11(2), 2017. Google Scholar
  21. Douglas Rodrigues, Xin-She Yang, André Nunes De Souza, and João Paulo Papa. Binary flower pollination algorithm and its application to feature selection. In Recent advances in swarm intelligence and evolutionary computation, pages 85-100. Springer, 2015. Google Scholar
  22. Muhammad Refaat Soliman and Rodolfo Pellizzoni. WCET-driven dynamic data scratchpad management with compiler-directed prefetching. In ECRTS, 2017. Google Scholar
  23. TeamPlay Consortium. Deliverable D4.5 - Report on Energy Usage Analysis and on Prototype - Version 1.0, 2020. Google Scholar
  24. Manish Verma and Peter Marwedel. Scratchpad Overlay Approaches for Main/Scratchpad Memory Hierarchy. In Advanced Memory Optimization Techniques for Low-Power Embedded Processors, pages 83-119. Springer, 2007. Google Scholar
  25. Xin-She Yang, Mehmet Karamanoglu, and Xingshi He. Flower pollination algorithm: a novel approach for multiobjective optimization. Engineering Optimization, 46(9):1222-1237, 2014. Google Scholar
  26. Eckart Zitzler. Evolutionary algorithms for multiobjective optimization: Methods and applications, volume 63. Citeseer, 1999. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact