Ahmed C. Ammari

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To cope with the variations and uncertainties that emanate from hardware and application characteristics, dynamic power management (DPM) frameworks must be able to learn about the system inputs and environment and adjust the power management policy on the fly. In this paper we present an online adaptive DPM technique based on model-free reinforcement(More)
H.264/AVC (Advanced Video Codec) is a new video coding standard developed by a joint effort of the ITU-TVCEG and ISO/IEC MPEG. This standard provides higher coding efficiency relative to former standards at the expense of higher computational requirements. Implementing the H.264 video encoder for an embedded System-on-Chip (SoC) is a big challenge. For an(More)
H.264/AVC (Advanced Video Codec) is a new video coding standard developed by a joint effort of the ITU-TVCEG and ISO/IEC MPEG. This standard provides higher coding efficiency relative to former standards at the expense of higher computational requirements. Implementing the H.264 video encoder for an embedded System-on-Chip (SoC) is a big challenge. For an(More)
H.264/AVC (Advanced Video Codec) is a new video coding standard developed by a joint effort of the ITU-TVCEG and ISO/IEC MPEG. This standard provides higher coding efficiency relative to former standards at the expense of higher computational requirements. Implementing the H.264 video encoder for an embedded Systemon-Chip (SoC) is thus a big challenge. For(More)
The evolution of digital video industry is being driven by continuous improvements in processing performance, availability of higher-capacity storage and transmission mechanisms. Getting digital video from its source (a camera or a stored clip) to its destination (a display) involves a chain of components. Key to this chain are the processes of compression(More)
This paper addresses the problem of extending battery service lifetime in a portable electronic system while maintaining an acceptable performance degradation level. The proposed dynamic power management (DPM) framework is based on model-free reinforcement learning (RL) technique. In this DPM framework, the Power Manager (PM) adapts the system operating(More)
The computational requirements for embedded applications are increasing exponentially. This complexity, coupled with constantly evolving specifications, has forced designers to consider intrinsically flexible implementations. In this paradigm, the digital system-on-a-chip platform-based design environment for shared memory multiple instructions multiple(More)
This paper presents a hierarchical dynamic power management (DPM) framework based on reinforcement learning (RL) technique, which aims at power savings in a computer system with multiple I/O devices running a number of heterogeneous applications. The proposed framework interacts with the CPU scheduler to perform effective application-level scheduling,(More)
Model-free reinforcement learning (RL) has become a promising technique for designing a robust dynamic power management (DPM) framework that can cope with variations and uncertainties that emanate from hardware and application characteristics. Moreover, the potentially significant benefit of performing application-level scheduling as part of the(More)