GOP-Level Dynamic Thermal Management in MPEG-2 Decoding

Lee, Wonbok; Patel, Kimish; Pedram, Massoud

doi:10.1109/tvlsi.2008.2000251

Cited by 17 publications

(6 citation statements)

References 22 publications

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“…Since this work belongs to the latter category, dynamic thermal management techniques are discussed in depth. A slack borrowing technique is proposed in [10] to dynamically manage peak temperature for MPEG-2 decoder. A reinforcementlearning based adaptive technique is proposed in [3] to optimize temperature by controlling task mapping based on the temperature of the current iteration.…”

Section: Related Workmentioning

confidence: 99%

“…Thermal management has attracted significant attention both in industry and academia. Examples include dynamic thermal management using voltage and frequency scaling [7], slack time management [10], peak temperature management through system-level task scheduling [3] and thermal stress management through application task mapping [2] (refer to Section 2 for a summary of related works). These approaches, however, suffer from the following limitations.…”

Section: Introductionmentioning

confidence: 99%

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Reinforcement Learning-Based Inter- and Intra-Application Thermal Optimization for Lifetime Improvement of Multicore Systems

Das

Shafik

Merrett

et al. 2014

Proceedings of the 51st Annual Design Automation Conference

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The thermal profile of multicore systems vary both within an application's execution (intra) and also when the system switches from one application to another (inter). In this paper, we propose an adaptive thermal management approach to improve the lifetime reliability of multicore systems by considering both inter-and intra-application thermal variations. Fundamental to this approach is a reinforcement learning algorithm, which learns the relationship between the mapping of threads to cores, the frequency of a core and its temperature (sampled from on-board thermal sensors). Action is provided by overriding the operating system's mapping decisions using affinity masks and dynamically changing CPU frequency using in-kernel governors. Lifetime improvement is achieved by controlling not only the peak and average temperatures but also thermal cycling, which is an emerging wear-out concern in modern systems. The proposed approach is validated experimentally using an Intel quad-core platform executing a diverse set of multimedia benchmarks. Results demonstrate that the proposed approach minimizes average temperature, peak temperature and thermal cycling, improving the mean-timeto-failure (MTTF) by an average of 2x for intra-application and 3x for inter-application scenarios when compared to existing thermal management techniques. Furthermore, the dynamic and static energy consumption are also reduced by an average 10% and 11% respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Inter- and Intra-Application Thermal Optimization for Lifetime Improvement of Multicore Systems

Das

Shafik

Merrett

et al. 2014

Proceedings of the 51st Annual Design Automation Conference

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“…We evaluate our TONE technique for three threshold temperatures , i.e. 54 ºC, 50 ºC and 46 ºC (as also used by state-of-the-art work [14]) showing how our technique adaptively controls the temperature. Figure 10-14 show the thermal curves during a part of the encoding process for the PartyScene, RaceHorses, Keiba, Basketball and BQMall sequences in terms of peak temperature-per-frame being encoded to take various scenarios into account.…”

Section: Resultsmentioning

confidence: 99%

“…In case of temperature violation, the policy reacts by selecting a new set of configuration parameters c based on our Pareto analysis that ensures temperature reduction with minimum losses in bit rate and PSNR (line 9). Finally, we encode the frame f with configuration c (line 10) update the current temperature (line 11), the error list (line 13) and the complexity of previous frame (line 14). Afterwards, the temperature, PSNR, and bit rate properties of the Pareto-optimal points are updated based on the currently video.…”

Section: Run-time Adaptive Temperature Optimizationmentioning

confidence: 99%

“…However, these general purpose state-of-the-art works do not account for application-specific properties and may incur loss in the video quality when applied to video processing systems. For specifically targeting the video encoding/decoding systems, works such as [13] and [14] use DVFS together with temporal and spatial degradation (by frame drops) as a mechanism to reduce the overall temperature. In these works the key target is to lower the temperature irrespective of the incurred video quality loss, which may be significant in presence of frame drops.…”

Section: Introductionmentioning

confidence: 99%

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Tone

Palomino

Shafique

Susin

et al. 2014

Proceedings of the 2014 International Symposium on Low Power Electronics and Design

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This paper presents an adaptive temperature optimization technique for the next generation video encoders. It exploits both applicationspecific knowledge (i.e. video encoding configurations) and video content properties in order to efficiently manage the temperature of advanced video coding systems at the software layer. For designing an efficient technique, we perform an extensive offline analysis to understand the impact of different video properties and configurations on the CPU thermal profiles when processing the next generation video encoder. Our temperature optimization technique performs an application-level prediction of the temperature trend followed by an application-level thermal management policy. The policy dynamically manages the temperature by performing an adaptive encoder configuration selection while providing minimum penalties in terms of bit rate and video quality. The experimental results show that our policy meets temperature constraints with negligible encoding performance loss. Moreover, when compared to state-of-the-art techniques, our policy provides a relatively reduced video quality loss while still meeting the temperature constraints.

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