Browsing by Author "Khernache, Mohammed Bey Ahmed"

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Energy-Aware HEVC software decoding on mobile heterogeneous Multi-Cores architectures
(Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 2022) Khernache, Mohammed Bey Ahmed; Boukhobza, Jalil; Benmoussa, Yahia; Menard, Daniel
Video content is becoming increasingly omnipresent on mobile platforms thanks to advances in mobile heterogeneous architectures. These platforms typically include limited rechargeable batteries which do not improve as fast as video content. Most state-of-the-art studies proposed solutions based on parallelism to exploit the GPP heterogeneity and DVFS to scale up/down the GPP frequency based on the video workload. However, some studies assume to have information about the workload before to start decoding. Others do not exploit the asymmetry character of recent mobile architectures. To address these two challenges, we propose a solution based on classification and frequency scaling. First, a model to classify frames based on their type and size is built during design-time. Second, this model is applied for each frame to decide which GPP cores will decode it. Third, the frequency of the chosen GPP cores is dynamically adjusted based on the output buffer size. Experiments on real-world mobile platforms show that the proposed solution can save more than 20% of energy (mJ/Frame) compared to the Ondemand Linux governor with less than 5% of miss-rate. Moreover, it needs less than one second of decoding to enter the stable state and the overhead represents less than 1% of the frame decoding time
HEVC hardware vs software decoding : an objective energy consumption analysis and comparison
(Elsevier, 2021) Khernache, Mohammed Bey Ahmed; Benmoussa, Yahia; Boukhobza, Jalil; Menard, Daniel
Web data are experiencing a proliferation of video content for mobile platforms. This is accompanied by new advances in heterogeneous general purpose processor (GPP) cores embedded in mobile devices which offer a great opportunity to enhance both performance and energy efficiency of software (SW) video decoding. On the other hand, hardware (HW) video accelerators are more energy-efficient but are not flexible and their time-to-market is significant. In this context, this paper proposes a characterization methodology to investigate the performance and power consumption of two video decoding approaches on mobile platforms. The first one uses a HW decoder intellectual property (HDIP) in addition to a GPP (for the control). The second one is SW-based and uses only a heterogeneous multi-core GPP. The objective is to study the behavior of both video decoding approaches by comparing them and to understand why and in which case it is worth relying on the GPP rather than the HDIP. We also derive the optimal GPP configuration (number of cores and their frequency) that minimizes the energy consumption for a given video bit-stream on a given platform. The proposed methodology was applied on the HEVC video codec standard. In some state-of-the-art work figures, the SW video decoding consumes up to more energy than HDIPs. Our results show that, for video resolutions of 1080p and lower and at the operating system perspective point of view, the HEVC SW decoding consumes on average less than more energy (mJ/Frame) than the HW one. Then, the more we scale up the resolution, the more we get the advantage of using the HW video decoding. Furthermore, the HEVC HW and SW decoders consume effectively less than 30% and 50% of the global power consumption of the tested platforms, respectively