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This work proposes a new dynamic thermal and reliability management framework via task mapping and migration to improve thermal performance and reliability of commercial multi-core processors considering workload-dependent thermal hot spot stress. The new method is motivated by the observation that different workloads activate different spatial power and thermal hot spots within each core of processors. Existing run-time thermal management, which is based on on-chip location-fixed thermal sensor information, can lead to suboptimal management solutions as the temperatures provided by those sensors may not be the true hot spots. The new method, called Hot-Trim, utilizes a machine learning-based approach to characterize the power density hot spots across each core, then a new task mapping/migration scheme is developed based on the hot spot stresses. Compared to existing works, the new approach is the first to optimize VLSI reliabilities by exploring workload-dependent power hot spots. The advantages of the proposed method over the Linux baseline task mapping and the temperature-based mapping method are demonstrated and validated on real commercial chips. Experiments on a real Intel Core i7 quad-core processor executing PARSEC-3.0 and SPLASH-2 benchmarks show that, compared to the existing Linux scheduler, core and hot spot temperature can be lowered by 1.15 to 1.31C. In addition, Hot-Trim can improve the chip's EM, NBTI and HCI related reliability by 30.2%, 7.0% and 31.1% respectively compared to Linux baseline without any performance degradation. Furthermore, it improves EM and HCI related reliability by 29.6% and 19.6% respectively, and at the same time even further reduces the temperature by half a degree compared to the conventional temperature-based mapping technique.
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HAT-DRL: Hotspot-Aware Task Mapping for Lifetime Improvement of Multicore System using Deep Reinforcement Learning
In this work, we proposed a novel learning-based task to core mapping technique to improve lifetime and reliability based on advanced deep reinforcement learning. The new method is based on the observation that on-chip temperature sensors may not capture the true hotspots of the chip, which can lead to sub-optimal control decisions. In the new method, we first perform data-driven learning to model the hotspot activation indicator with respect to the resource utilization of different workloads. On top of this, we proposed to employ a recently proposed, highly robust, sample-efficient soft-actor-critic deep reinforcement learning algorithm, which can learn optimal maximum entropy policies to improve the long-term reliability and minimize the performance degradation from NBTI/HCI effects. Lifetime and reliability improvement is achieved by assigning a reward function, which penalizes continuously stressing the same hotspots and encourages even stressing of cores. The proposed algorithm is validated with an Intel i7-8650U four-core CPU platform executing CPU benchmark workloads for various hotspot activation profiles. Our experimental results show that the proposed method balances the stress between all cores and hotspots, and achieves 50% and 160% longer lifetime compared to non-hotspot-aware and Linux default scheduling respectively. The proposed method can also reduce the average temperature by exploiting the true-hotspot information.
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- Award ID(s):
- 1854276
- PAR ID:
- 10275546
- Date Published:
- Journal Name:
- Proc. 2nd IEEE/ACM Workshop on Machine Learning for CAD
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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