Abstract
Domain-specific systems-on-chip (DSSoCs) aim at bridging the gap between application-specific integrated circuits (ASICs) and general-purpose processors. Traditional operating system (OS) schedulers can undermine the potential of DSSoCs since their execution times can be orders of magnitude larger than the execution time of the task itself. To address this problem, we propose a dynamic adaptive scheduling (DAS) framework that combines the benefits of a fast (low-overhead) scheduler and a slow (sophisticated, high-performance but high-overhead) scheduler. Experiments with five real-world streaming applications show that DAS consistently outperforms both the fast and slow schedulers. For 40 different workloads, DAS achieves 1.29× speedup and 45% lower EDP than the sophisticated scheduler at low data rates and 1.28× speedup and 37% lower EDP than the fast scheduler when the workload intensifies.
Original language | English (US) |
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Pages (from-to) | 51-54 |
Number of pages | 4 |
Journal | IEEE Embedded Systems Letters |
Volume | 14 |
Issue number | 1 |
DOIs | |
State | Published - Mar 1 2022 |
Externally published | Yes |
Keywords
- Domain-specific system-on-chip (DSSoC)
- machine learning
- policy switching
- runtime classification
- task scheduling
ASJC Scopus subject areas
- Control and Systems Engineering
- General Computer Science