Inter-Procedural Strength Reduction for Embedded Systems

Agosta, Giovanni

doi:10.4230/OASIcs.PARMA-DITAM.2026.4

File

Subject Classification

ACM Subject Classification

Computer systems organization → Embedded software
Software and its engineering → Compilers

Keywords

Compiler Optimization
Strength Reduction

Metrics

Access Statistics
Total Accesses (updated on a weekly basis)

0

Document

0

Metadata

Abstract

Embedded systems often rely on code generated from model-based design tools, which can result in inefficient implementations due to the loss of high-level semantic information during code generation. This paper explores an inter-procedural extension of the strength reduction transformation, traditionally applied within loops, to optimize repeated computations across function calls. The proposed technique identifies parameters acting as counters and replaces costly operations - such as exponentiation or multiplication - with incremental updates based on recurrence relations, using static variables to preserve state between calls. We formalize the transformation, discuss its applicability conditions, and analyse tradeoffs between computation and memory access costs. Experimental evaluation on ARMv8, AVR microcontrollers, and x86_64 platforms demonstrates significant speedups for power operations (up to 9 ×), while highlighting limitations for simpler operations due to memory overhead.

Cite As Get BibTex

Giovanni Agosta. Inter-Procedural Strength Reduction for Embedded Systems. In 17th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 15th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2026). Open Access Series in Informatics (OASIcs), Volume 141, pp. 4:1-4:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/OASIcs.PARMA-DITAM.2026.4

Author Details

Giovanni Agosta

Politecnico di Milano, Italy
Consorzio Interuniversitario Nazionale per l'Informatica, Roma, Italy

References

Giovanni Agosta, Emanuele Baldino, Francesco Casella, Stefano Cherubin, Alberto Leva, Federico Terraneo, et al. Towards a high-performance modelica compiler. In Proceedings of the 13th International Modelica Conference, pages 313-320, 2019. URL: https://doi.org/10.3384/ecp19157313.
Giovanni Agosta, Alessandro Barenghi, and Gerardo Pelosi. Compiler-based techniques to secure cryptographic embedded software against side-channel attacks. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 39(8):1550-1554, 2019. URL: https://doi.org/10.1109/TCAD.2019.2912924.
Deniz Akdur. Skills gaps in the industry: Opinions of embedded software practitioners. ACM Transactions on Embedded Computing Systems (TECS), 20(5):1-39, 2021. URL: https://doi.org/10.1145/3463340.
Deniz Akdur, Bilge Say, and Onur Demirörs. Modeling cultures of the embedded software industry: feedback from the field. Software and Systems Modeling, 20(2):447-467, 2021. URL: https://doi.org/10.1007/s10270-020-00810-9.
David F Bacon, Susan L Graham, and Oliver J Sharp. Compiler transformations for high-performance computing. ACM Computing Surveys (CSUR), 26(4):345-420, 1994. URL: https://doi.org/10.1145/197405.197406.
Jiahong Bi, Guilherme Korol, and Jeronimo Castrillon. Leveraging the mlir infrastructure for the computing continuum. In CPS Workshop 2024, September 2024. URL: https://ceur-ws.org/Vol-3960/short5.pdf.
Daniele Cattaneo, Michele Chiari, Giovanni Agosta, and Stefano Cherubin. Taffo: The compiler-based precision tuner. SoftwareX, 20:101238, 2022. URL: https://doi.org/10.1016/j.softx.2022.101238.
Daniele Cattaneo, Michele Chiari, Nicola Fossati, Stefano Cherubin, and Giovanni Agosta. Architecture-aware precision tuning with multiple number representation systems. In 2021 58th ACM/IEEE Design Automation Conference (DAC), pages 673-678. IEEE, 2021. URL: https://doi.org/10.1109/DAC18074.2021.9586303.
Stefano Cherubin and Giovanni Agosta. Tools for reduced precision computation: a survey. ACM Computing Surveys (CSUR), 53(2):1-35, 2020. URL: https://doi.org/10.1145/3381039.
Lev Denisov, Gabriele Magnani, Daniele Cattaneo, Giovanni Agosta, and Stefano Cherubin. The impact of profiling versus static analysis in precision tuning. IEEE Access, 12:69475-69487, 2024. URL: https://doi.org/10.1109/ACCESS.2024.3401831.
Jack Ganssle. The art of designing embedded systems. Newnes, 2008.
W.H. Harrison. Compiler analysis of the value ranges for variables. IEEE Transactions on Software Engineering, SE-3(3):243-250, 1977. URL: https://doi.org/10.1109/TSE.1977.231133.
Yew Ho Hee, Mohamad Khairi Ishak, Mohd Shahrimie Mohd Asaari, and Mohamad Tarmizi Abu Seman. Embedded operating system and industrial applications: a review. Bulletin of Electrical Engineering and Informatics, 10(3):1687-1700, 2021.
Chris Lattner and Vikram Adve. Llvm: A compilation framework for lifelong program analysis & transformation. In International symposium on code generation and optimization, 2004. CGO 2004., pages 75-86. IEEE, 2004. URL: https://doi.org/10.1109/CGO.2004.1281665.
Chris Lattner, Mehdi Amini, Uday Bondhugula, Albert Cohen, Andy Davis, Jacques Pienaar, River Riddle, Tatiana Shpeisman, Nicolas Vasilache, and Oleksandr Zinenko. Mlir: Scaling compiler infrastructure for domain specific computation. In 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), pages 2-14. IEEE, 2021. URL: https://doi.org/10.1109/CGO51591.2021.9370308.
Peter Liggesmeyer and Mario Trapp. Trends in embedded software engineering. IEEE Software, 26(3):19-25, 2009. URL: https://doi.org/10.1109/MS.2009.80.
Gabriele Magnani, Daniele Cattaneo, Michele Chiari, Giovanni Agosta, et al. The impact of precision tuning on embedded systems performance: A case study on field-oriented control. In Proceedings of PARMA-DITAM 2021 (OASICS), volume 88, pages 1-13, 2021. URL: https://doi.org/10.4230/OASIcs.PARMA-DITAM.2021.3.
John A Stankovic. Real-time and embedded systems. ACM Computing Surveys (CSUR), 28(1):205-208, 1996. URL: https://doi.org/10.1145/234313.234400.
Theodoros Theodoridis, Tobias Grosser, and Zhendong Su. Understanding and exploiting optimal function inlining. In Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, pages 977-989, 2022. URL: https://doi.org/10.1145/3503222.3507744.
Nikita Veshchikov and Sylvain Guilley. Use of simulators for side-channel analysis. In 2017 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), pages 104-112, 2017. URL: https://doi.org/10.1109/EuroSPW.2017.59.
Yutong Wang and Cindy Rubio-González. Predicting performance and accuracy of mixed-precision programs for precision tuning. In Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, pages 1-13, 2024. URL: https://doi.org/10.1145/3597503.3623338.