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A tool for modeling and simulation of approximate computing in hardware

Grant number: 17/08015-8
Support type:Scholarships in Brazil - Master
Effective date (Start): October 01, 2017
Effective date (End): February 28, 2019
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Lucas Francisco Wanner
Grantee:Isaías Bittencourt Felzmann
Home Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil


Approximate computing is a set of techniques that explore the generation of potentially incorrect or inaccurate results, when obtaining the most accurate result is not necessary or efficient for the given application. Approximate computing techniques have emerged in hardware, algorithms and applications development. Recent research shows that getting an approximate, but sufficient for the application, result is often less costly - specially in terms of energy consumption - than getting an accurate result. Despite the substantial interest in this research field, there is a lack of tools to support approximate hardware systems development. In this project, we propose a tool to explore approximate architectures. This tool, based in the ArchC architecture description language, will allow the exploitation of processors and systems where the result of hardware operations is approximated. These approximations will be derived from both the development of hardware components where the results are deterministic and approximated and the variation of parameters that are critical for the result reliability, such as temperature and source voltage. The tool will be launched as an opensource module, and will be verified using approximation-tolerant software as case studies, pointing design choices and the trade-off between quality of result and energy consumption. (AU)

Academic Publications
(References retrieved automatically from State of São Paulo Research Institutions)
FELZMANN, Isaías Bittencourt. Uma ferramenta para modelagem e simulação de computação aproximada em hardware. 2019. Master's Dissertation.

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