Thermal and QoS-aware Embedded Systems

As smartphones/cars and IoT devices become embedded in every aspect of our lives, these embedded systems face unprecedented challenges in their thermal management. Extreme thermal conditions accompany threats that are no longer limited to the cyber-space but even risk physical safety; high device temperature can cause user discomforts, device failures, or battery explosions. As users repeatedly express concern about the associated thermal issues, embedded systems in the wild, requires the adaptive, effective and practical solutions to overcome these threats. Our research aims to bridge the gap between the systems and thermal dynamics. By capturing such dual-dynamics, our proposed systems can adapt to the changing thermal conditions in real-time. Building on the analysis of application workloads and underlying hardware, it is effective in satisfying application QoS and battery/thermal requirements. Finally, we made my proposed systems practical to deploy by implementing and demonstrating them on real-world smartphones or embedded controllers used in cars. The systems span various environments and platforms. They address chip overheating issues under changing environment (RT-TRM), and on integrated CPUs-GPU (RT-TAS) for automotive real-time systems, and develop embedded cooling solution (eTEC) for smartphones.

Real-time Thermal-Aware Resource Management
With an increasing demand for complex and powerful System-on-Chips (SoCs), modern real-time automotive systems face significant challenges in managing on-chip temperature. We demonstrate, via real experiments, the importance of accounting for dynamic ambient temperature and task-level power dissipation in resource management so as to meet both thermal and timing constraints. To address this problem, we propose RT-TRM, a real-time thermal-aware resource management framework. We first introduce a task-level dynamic power model that can capture different power dissipations with a simple task-level parameter called the activity factor. We then develop two new mechanisms, adaptive parameter assignment and online idle-time scheduling. The former adjusts voltage/frequency levels and task periods according to the varying ambient temperature while preserving feasibility. The latter generates a schedule by allocating idle times efficiently without missing any task/job deadline. By tightly coupling the solutions of these two mechanisms, we can guarantee both thermal and timing constraints in the presence of dynamic ambient temperature variations.

Thermal-Aware Scheduling for Integrated CPUs–GPU Platforms
As modern embedded systems like cars need high-power integrated CPUs–GPU SoCs for various real-time applications such as lane or pedestrian detection, they face greater thermal problems than before, which may, in turn, incur higher failure rate and cooling cost. We demonstrate, via experimentation on a representative CPUs– GPU platform, the importance of accounting for two distinct thermal characteristics — the platform’s temperature imbalance and different power dissipations of different tasks — in real-time scheduling to avoid any burst of power dissipations while guaranteeing all timing constraints. To achieve this goal, we propose a new Real-Time Thermal-Aware Scheduling (RT-TAS) framework. We first capture different CPU cores’ temperatures caused by different GPU power dissipations (i.e., CPUs–GPU thermal coupling) with core-specific thermal coupling coefficients. We then develop thermally-balanced task-to-core assignment and CPUs–GPU co-scheduling. The former addresses the platform’s temperature imbalance by efficiently distributing the thermal load across cores while preserving scheduling feasibility. Building on the thermally-balanced task assignment, the latter cooperatively schedules CPU and GPU computations to avoid simultaneous peak power dissipations on both CPUs and GPU, thus mitigating excessive temperature rises while meeting task deadlines.

Efficient Thermoelectric Cooling
Mobile apps suffer large performance degradation when the underlying processors are throttled to cool down the devices. Fans or heat sinks are not a viable option for mobile devices, thus calling for a new portable cooling solution. Thermoelectric coolers are scalable and controllable cooling devices that can be embedded into mobile devices on the chip surface. This paper presents a thermoelectric cooling solution that enables efficient processor thermal management in mobile devices. Our goal is to minimize performance loss from thermal throttling by efficiently using thermoelectric cooling. Since mobile devices experience large variations in workloads and ambient temperature, our solution adaptively controls cooling power at runtime.

Faculty

  • Kang G. Shin

Graduate Students

  • Youngmoon Lee

External Collaborators


Funding Sources

  • National Research Foundation of Korea

Publications

  • Youngmoon Lee, Hoonsung Chwa and Kang G. Shin, Thermal-Aware Scheduling for Integrated CPUs-GPU platforms, in the ACM SIGBED International Conference on Embedded Software (EMSOFT’19), New York, NY, USA, October 2019.
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  • Youngmoon Lee, Hoon Sung Chwa, Kang G. Shin, and Shige Wang, Thermal-Aware Resource Management for Embedded Real-Time Systems, in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 37, no. 11, pp. 2857-2868, November 2018.
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  • Youngmoon Lee, Hoonsung Chwa, Kang G. Shin, and Shige Wang, Thermal-Aware Resource Management for Embedded Real-Time Systems, in EMSOFT 2018, Torino, Italy, September 2018.
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  • Youngmoon Lee, Eugene Kim, and Kang G. Shin, Efficient Thermoelectric Cooling for Mobile Devices, in the 2017 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED '17), Taipei, Taiwan, July 2017.
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