Leveraging Prior Knowledge for Effective Design-Space Exploration in High-Level Synthesis
| Autor: | Jihye Kwon, Luca P. Carloni, Giuseppe Di Guglielmo, Giovanni Ansaloni, Laura Pozzi, Lorenzo Ferretti |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
Design space exploration Pareto principle 02 engineering and technology Computer Graphics and Computer-Aided Design 020202 computer hardware & architecture Microarchitecture Computer engineering High-level synthesis Metric (mathematics) 0202 electrical engineering electronic engineering information engineering Electronic design automation Electrical and Electronic Engineering Representation (mathematics) Implementation Software |
| Zdroj: | IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 39:3736-3747 |
| ISSN: | 1937-4151 0278-0070 |
| DOI: | 10.1109/tcad.2020.3012750 |
| Popis: | High-Level Synthesis (HLS) tools allow the generation of a large variety of hardware implementations from the same specification by setting different optimization directives. Each combination of HLS directives returns an implementation of the target application that is based on a particular microarchitecture. Designers are interested only in the subset of implementations that correspond to Pareto-optimal points in the performance versus cost design space. Finding this subset is hard because the relationship between the HLS directives and the Pareto-optimal implementations cannot be foreseen. Hence, designers must default to an exploration of the design space through many time-consuming HLS runs. We present a methodology that infers knowledge from past design explorations to identify high-quality directives for new target applications. To this end, we formulate a novel abstract representation of applications and their associated configuration spaces, introduce a similarity metric to compare quantitatively the configuration spaces of different applications, and a method to infer actionable information from a source space to a target space. The experimental results with the MachSuite benchmarks show that our approach retrieves close approximations of the Pareto frontier of best-performing implementations for the target application, in exchange for a small number of HLS runs. |
| Databáze: | OpenAIRE |
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