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Integrated scratchpad memory optimization and task scheduling for MPSoC architectures
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Source International Conference on Compilers, Architecture and Synthesis for Embedded Systems archive
Proceedings of the 2006 international conference on Compilers, architecture and synthesis for embedded systems table of contents
Seoul, Korea
SESSION: Robustness table of contents
Pages: 401 - 410  
Year of Publication: 2006
ISBN:1-59593-543-6
Authors
Vivy Suhendra  National University of Singapore
Chandrashekar Raghavan  National University of Singapore
Tulika Mitra  National University of Singapore
Sponsors
SIGDA: ACM Special Interest Group on Design Automation
ACM: Association for Computing Machinery
SIGBED: ACM Special Interest Group on Embedded Systems
SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing
Publisher
ACM  New York, NY, USA
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ABSTRACT

Multiprocessor system-on-chip (MPSoC) is an integrated circuit containing multiple instruction-set processors on a single chip that implements most of the functionality of a complex electronic system. An MPSoC architecture is, in general, customized for an embedded application. A critical component of this customization process is the on-chip memory system configuration. Embedded systems increasingly employ software-controlled scratchpad memory(SPM) due to its inherent advantages in terms of area, energy, and timing predictability compared to caches. An application-specific flexible partitioning of the on-chip SPM budget among the processors is critical for performance optimization. Moreover, scheduling the tasks of an application on to the processors and partitioning the SPM are inter-dependent even though these steps are decoupled in the traditional design space exploration process. In this work, we design an integrated task mapping, scheduling, SPM partitioning, and data allocation technique based on Integer Linear Programming(ILP)formulation. Our ILP formulation explores the optimal performance limit and shows that integrated task schedul-ing and SPM optimization improves performance by up to 80% for embedded applications.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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L. Benini, D. Bertozzi, A. Guerri, and M. Milano. Allocation and scheduling for mpsocs via decomposition and no-good generation. In International Joint Conferences on Artificial Intelligence (IJCAI), 2005.
 
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Collaborative Colleagues:
Vivy Suhendra: colleagues
Chandrashekar Raghavan: colleagues
Tulika Mitra: colleagues