Download Algorithm-Architecture Matching for Signal and Image by Xiaolin Chen, Nishan Canagarajah, Jose L. Nunez-Yanez PDF

By Xiaolin Chen, Nishan Canagarajah, Jose L. Nunez-Yanez (auth.), Guy Gogniat, Dragomir Milojevic, Adam Morawiec, Ahmet Erdogan (eds.)

Advances in sign and picture processing including expanding computing energy are bringing cellular expertise toward functions in numerous domain names like automobile, health and wellbeing, telecommunication, multimedia, leisure and so on. the improvement of those top functions, concerning a wide range of algorithms (e.g. sign, snapshot, video, 3D, conversation, cryptography) is classically divided into 3 consecutive steps: a theoretical learn of the algorithms, a examine of the objective structure, and eventually the implementation. one of these linear layout movement is attaining its limits because of excessive strain on layout cycle and strict functionality constraints. The procedure, known as Algorithm-Architecture Matching, goals to leverage layout flows with a simultaneous examine of either algorithmic and architectural matters, taking into consideration a number of layout constraints, in addition to set of rules and structure optimizations, that couldn’t be completed differently if thought of individually. Introducing new layout methodologies is vital while dealing with the recent rising functions as for instance complex cellular conversation or photos utilizing sub-micron production applied sciences or 3D-Integrated Circuits. This range kinds a driver for the longer term evolutions of embedded approach designs methodologies.

The major expectancies from process designers’ perspective are concerning equipment, instruments and architectures helping software complexity and layout cycle relief. complex optimizations are necessary to meet layout constraints and to allow a large attractiveness of those new technologies.

Algorithm-Architecture Matching for sign and photograph Processing offers a set of chosen contributions from either and academia, addressing diverse features of Algorithm-Architecture Matching method starting from sensors to architectures layout. The scope of this ebook displays the range of strength algorithms, together with sign, communique, snapshot, video, 3D-Graphics carried out onto quite a few architectures from FPGA to multiprocessor structures. a number of synthesis and source administration suggestions leveraging layout optimizations also are defined and utilized to varied algorithms.

Algorithm-Architecture Matching for sign and snapshot Processing might be on every one designer’s and EDA device developer’s shelf, in addition to on people with an curiosity in electronic approach layout optimizations facing complex algorithms.

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Read or Download Algorithm-Architecture Matching for Signal and Image Processing: Best papers from Design and Architectures for Signal and Image Processing 2007 & 2008 & 2009 PDF

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Extra info for Algorithm-Architecture Matching for Signal and Image Processing: Best papers from Design and Architectures for Signal and Image Processing 2007 & 2008 & 2009

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Ratakonda K, Ahuja N (2002) Lossless image compression with multiscale segmentation. IEEE Trans Image Process 11(11):1228–1237 21. Ryan MJ, Arnold JF (1997) The lossless compression of AVIRIS images by vector quantization. IEEE Trans Geosci Remote Sens 35(3):546–550 22. Said A (2004) Introduction to arithmetic coding – theory and practice. Imaging Systems Laboratory, HP Laboratories Palo Alto 23. Saunders D, Cupitt J (2003) Image processing at the national gallery: the VASARI project. The National Gallery, Technical Bulletin 14(1):72–85.

Direct volume representations, on the other hand, involve partitioning the space into small cubic fragments called voxels, and storing a meaningful value for each voxel, such as color, density, and so on. This is especially useful in medical visualization and imaging. With no loss of generality, we choose the later direct volume representation as our performance case study. This chapter demonstrates the usefulness of the nD-AP Cache architecture (nDimensional Adaptive and Predictive Cache) [12, 15] and the associated methodology for some ray-shooting based applications.

12 RCPG-U visualization efficiency: the 3D-AP Cache efficiency for each beam of rays. The cache efficiency exceeds 90% for some tiles (white is 100%) drops above this threshold. Figure 11 provides the average cache efficiency along the memory latency for two viewpoints and two resolutions. The computed images are divided in tiles of 12 × 12 pixels, to form beams of 144 rays: at low resolution there are 22 × 22 tiles and 44 × 44 tiles at the high resolution. The figure also provides the size of the cache memory.

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