Abstract
Modern systems-on-chip (SoCs) use dynamic power management (DPM) techniques to improve energy efficiency. However, existing techniques are unable to efficiently adapt the runtime decisions considering multiple objectives (e.g., energy and real-time requirements) simultaneously on heterogeneous platforms. To address this need, we propose HiLITE, a hierarchical imitation learning framework that maximizes the energy efficiency while satisfying soft real-time constraints on embedded SoCs. Our approach first trains DPM policies using imitation learning; then, it applies a regression policy at runtime to minimize deadline misses. HiLITE improves the energy-delay product by 40 percent on average, and reduces deadline misses by up to 76 percent, compared to state-of-the-art approaches. In addition, we show that the trained policies not only achieve high accuracy, but also have negligible prediction time overhead and small memory footprint.
Original language | English (US) |
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Article number | 9085952 |
Pages (from-to) | 63-67 |
Number of pages | 5 |
Journal | IEEE Computer Architecture Letters |
Volume | 19 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2020 |
Keywords
- Dynamic power management
- Hierarchical imitation learning
- Machine learning
- Real-time
- SoC
ASJC Scopus subject areas
- Hardware and Architecture