Hierarchical and Lightweight Imitation Learning for Dynamic Power Management of Embedded SoCs
Systems-on-chip (SoCs) should be designed to meet aggressive performance requirements while coping with limited battery capacity, thermal design power (TDP), and real-time (RT) constraints. A step in this direction consists of exploiting heterogeneity, e.g., using big cores when high performance is needed and switching to little cores otherwise. In addition, techniques such as dynamic voltage and frequency scaling (DVFS) and power gating (PG) can be used at runtime to manage the power consumption of SoCs. However, the design space of runtime decisions explodes combinatorially with the number of cores, frequency levels, and power states. Additionally, current platforms serve a wide range of applications with distinct characteristics and requirements. Thus, the immense design space and the growing variety of applications call for new runtime techniques to efficiently manage the power and performance of embedded heterogeneous platforms.
Researchers at Arizona State University, the University of Wisconsin-Madison, the University of Texas at Austin, the University of Arizona, and Carnegie Mellon University, have developed a hierarchical imitation learning framework that maximizes the energy efficiency while satisfying soft real-time constraints on embedded SoCs. This approach first trains dynamic power management (DPM) policies using imitation learning; then, it applies a regression policy at runtime to minimize deadline misses.
• Embedded systems-on-chip (SoCs) including applications in wireless communications and radar systems
Benefits and Advantages
• Improves the energy-delay product by an average of 40%
• Reduces deadline misses by up to 76% compared to state-of-the-art approaches
• Trained policies impose negligible prediction time overhead and minimal memory footprint
Related publication: HiLITE: Hierarchical and Lightweight Imitation Learning for Power Management of Embedded SoCs
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Inventor(s):Umit Ogras Radu Marculescu Ali Akoglu Chaitali Chakrabarti Daniel Bliss Samet Arda Anderson Sartor Nirmal Kumbhare Anish Krishnakumar Joshua Mack Ahmet Goksoy Sumit Mandal
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- Shen Yan