Reducing DRAM Image Data Access Energy Consumption in Video Processing

Li, Yiran; Zhang, Tong

doi:10.1109/tmm.2011.2177079

Cited by 13 publications

(10 citation statements)

References 22 publications

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“…[14,15]) or energy consumption (e.g. [16]), the proposed architecture offers both. Secondly, the overhead of implementing the architecture is minimal.…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

“…Fig. 8 a, b Comparison between ground truth image and the reconstruction using pixels sampled at a threshold of 600. c Reconstruction PSNR versus percentage of pixels sampled; d SDRAM access energy versus reconstruction PSNR; e SDRAM access time versus reconstruction PSNR; from left to right, the data points of HWdriven adaptive refine and full adaptive sampling algorithms in these graphs are results from threshold of 1800, 1300, 900, 600, 400, 300, 200, and 150, respectively; the data points of uniform refine algorithm are results from sampling rate of 16,8,4, and 2, respectively…”

Section: Evaluation Of the Proposed Architecture On Reconfigurable Plmentioning

confidence: 99%

“…This leads to various researches and developments in dealing with the cost of the image acquisition process. These efforts include general solutions such as the development of memory hierarchy, and image specific solutions such as access pattern optimization [16], and application code rewriting [4], etc.…”

Section: Introductionmentioning

confidence: 99%

“…The conventional access pattern sees all pixels of an image macro block read from the memory row by row and stored in the local buffer to be access by the client unit. According to the structure of memory systems, optimizations have been made to accelerate the process and/or lower its energy consumption by reorganising the access patterns [10,16]. There are also works that construct memory hierarchy specifically for image processing applications [7] and memory structures dedicated to image processing applications [11,12].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Context-based image acquisition from memory in digital systems

Liu

Bouganis

Cheung

2016

J Real-Time Image Proc

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A key consideration in the design of image and video processing systems is the ever increasing spatial resolution of the captured images, which has a major impact on the performance requirements of the memory subsystem. This is further amplified by the facts that the memory bandwidth requirements and energy consumption of accessing the captured images have started to become the bottlenecks in the design of high-performance image processing systems. Inspired by the successful application of progressive image sampling techniques in various image processing tasks, this work proposes the concept of Context-based Image Acquisition for hardware systems that efficiently trades image quality for reduced cost of the image acquisition process. Based on the proposed framework, a hardware architecture is developed which alters the conventional memory access pattern, to progressively and adaptively access pixels from a memory subsystem. The sampled pixels are used to reconstruct an approximation to the ground truth, which is stored in a high-performance image buffer for further processing. An instance of the architecture is prototyped on an FPGA and its performance evaluation shows that a saving of up to 85 % of memory accessing time and 33 %/45 % of image acquisition time/ energy are achieved on a set of benchmarks while maintaining a high PSNR.

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“…[14,15]) or energy consumption (e.g. [16]), the proposed architecture offers both. Secondly, the overhead of implementing the architecture is minimal.…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

Section: Evaluation Of the Proposed Architecture On Reconfigurable Plmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Context-based image acquisition from memory in digital systems

Liu

Bouganis

Cheung

2016

J Real-Time Image Proc

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“…On the other hand, the spatial locality of image data transforms into temporal correlation of signals on data bus. Therefore data bus energy consumption during image accessing is also studied on top of general purpose computing systems [4]. There are also dedicated image accessing systems built for specific image processing tasks such as video decoding process, where the energy consumption of the memory accessing process is even more dominant than that of the decoding process in certain situations [5].…”

Section: Introductionmentioning

confidence: 99%

Image progressive acquisition for hardware systems

Liu¹,

Bouganis²,

Cheung³

2014

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2014

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As the resolution of digital images increases, accessing raw image data from memory has become a major consideration during the design of image/video processing systems. This is due to the fact that the bandwidth requirement and energy consumption of such image accessing process has increased. Inspired by the successful application of progressive image sampling techniques in many image processing tasks, this work proposes to apply similar concept within hardware systems to efficiently trade image quality for reduced memory bandwidth requirement and lower energy consumption. Based on this idea, a hardware system is proposed that is placed between the memory subsystem and the processing core of the design. The proposed system alters the conventional memory access pattern to progressively and adaptively access pixels from a target memory external to the system. The sampled pixels are used to reconstruct an approximation to the ground truth, which is stored in an internal image buffer for further processing. The system is prototyped on FPGA and its performance evaluation shows that a saving of up to 85% of memory accessing time and 33%/45% of image acquisition time/energy is achieved on the benchmark image "lena" while maintaining a PSNR of about 30 dB.

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