2 results
Search Results
Now showing 1 - 2 of 2
Item Turning Visual Noise Into Hardware Efficiency: Systems of Viewer and Content Aware Power-Quality Scalable Embedded Memories With ECC-Adaptation for Big Videos and Deep Learning(North Dakota State University, 2021) Haidous, Ali AhmadMobile devices, such as smart phones, are being increasingly utilized for watching videos. Video processing requires frequent memory access that consume a significant amount of power due to large data size and intensive computational requirements. This limits battery life and frustrates users. Memory designers are focused on hardware-level power-optimization techniques without consideration of how hardware performance influences viewers' actual experience. The human visual system is limited in its ability to detect subtle degradations in image quality. For example, under conditions of high ambient illumination – such as outdoors in direct sunlight – the veiling luminance (i.e., glare) on the screen of a mobile device can effectively mask imperfections in the image. Under these circumstances, a video can be rendered in lower than full quality without the viewer being able to detect any difference in quality. As a result, the isolation between hardware design and viewer experience significantly increases hardware implementation overhead and power consumption due to overly pessimistic design margins, while integrating the two would have the opposite effect. In this dissertation, viewer-awareness, content-awareness, and hardware adaptation are integrated to achieve power optimization without degrading video quality, as perceived by users. Specifically, this dissertation will (i) experimentally and mathematically connect viewer experience, ambient illuminance, and memory performance; (ii) develop energy-quality adaptive hardware that can adjust memory usage based on ambient luminance to reduce power usage without impacting viewer experience; (iii) design various mobile video systems to fully evaluate the effectiveness of the developed methodologies; and (iv) provide an overview of bleeding edge related area research then push the boundary further using the novel techniques discussed to achieve optimized quality, silicone area overhead, and power reduction in video memory.Item Intelligent Energy-Efficient Storage System for Big-Data Applications(North Dakota State University, 2020) Gong, YifuStatic Random Access Memory (SRAM) is a critical component in mobile video processing systems. Because of the large video data size, the memory is frequently accessed, which dominates the power consumption and limits battery life. In energy-efficient SRAM design, a substantial amount of research is presented to discuss the mechanisms of approximate storage, but the content and environment adaptations were never a part of the consideration in memory design. This dissertation focuses on optimization methods for the SRAM system, specifically addressing three areas of Intelligent Energy-Efficient Storage system design. First, the SRAM stability is discussed. The relationships among supply voltage, SRAM transistor sizes, and SRAM failure rate are derived in this section. The result of this study is applied to all of the later work. Second, intelligent voltage scaling techniques are detailed. This method utilizes the conventional voltage scaling technique by integrating self-correction and sizing techniques. Third, intelligent bit-truncation techniques are developed. Viewing environment and video content characteristics are considered in the memory design. The performance of all designed SRAMs are compared to published literature and are proven to have improvement.