Dataset - Automated Design Space Exploration of Approximation-Enabled Accelerators: A Systematic Literature Mapping

Data collected for systematic literature mapping on design space exploration strategies for approximate computing accelerators.

Abstract— This paper provides a systematic mapping of
the literature regarding automated Design Space Exploration
(DSE) for error-tolerant systems, where Approximate Com-
puting (AxC) is employed to provide gains in energy efficiency
or performance. Starting from the Scopus aggregator and
IEEE Xplore library, 1302 texts were analyzed, from which 43
peer-reviewed research works were selected to map the state-
of-the-art in DSE techniques for designing approximate hard-
ware accelerators. The selected papers were used to catalog
the most commonly used search strategies, the methods for in-
tegrating approximate units and techniques in the design space
of a given system, and challenges and opportunities for the us-
age of automated DSE in AxC-enabled workflows. The results
highlight the usage of genetic and evolutionary algorithms, and
identify a trend for tree search-based exploration, with a focus
on selection of arithmetic units and functional approximation.
This study also shows the challenge of integrating a DSE stage
in existing workflows, particularly due to the difficulty of an-
alyzing error and electrical characteristics with feasible run-
times.