Efficient Search-Space Encoding for System-Level Design Space Exploration of Embedded Systems

Richthammer, Valentina; Glab, Michael

doi:10.1109/mcsoc.2019.00046

Cited by 3 publications

(1 citation statement)

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“…The work from Richthammer and others [26,27,25] is very similar in nature to the applications discussed in this paper. They also aim to improve Design-Space Exploration (DSE) methods by statically exploiting the architectural structure, although the concrete structure they exploit is different.…”

Section: Related Workmentioning

confidence: 63%

Embeddings of Task Mappings to Multicore Systems

Goens

Castrillón

2022

Lecture Notes in Computer Science

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The problem of finding good mappings is central to designing and executing applications efficiently in embedded systems. In heterogeneous multicores, which are ubiquitous today, this problem yields an intractably large design space of possible mappings. Most methods explore this space using heuristics, many of which implicitly use geometric notions in mappings. In this paper we explore the geometry of the mapping problem explicitly, for finding embeddings of the mapping space that capture its structure. This allows us to formulate new mapping strategies by leveraging the geometry of the mapping space, as well as improving existing heuristics that do so implicitly. We evaluate our approach on a novel mapping heuristic based on gradient descent, as well as multiple existing meta-heuristics. For complex architectures, our methods improved the results of established exploration meta-heuristics by about an order of magnitude in average.

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Section: Related Workmentioning

confidence: 63%