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 in the direction of functions in a number of domain names like automobile, well-being, telecommunication, multimedia, leisure etc. the advance of those prime functions, related to a wide variety of algorithms (e.g. sign, photograph, video, 3D, verbal exchange, cryptography) is classically divided into 3 consecutive steps: a theoretical research of the algorithms, a learn of the objective structure, and eventually the implementation. this type of linear layout move is achieving its limits as a result of severe strain on layout cycle and strict functionality constraints. The procedure, referred to as Algorithm-Architecture Matching, goals to leverage layout flows with a simultaneous research of either algorithmic and architectural concerns, considering a number of layout constraints, in addition to set of rules and structure optimizations, that couldn’t be accomplished in a different way if thought of individually. Introducing new layout methodologies is crucial while dealing with the hot rising purposes as for instance complicated cellular communique or photographs utilizing sub-micron production applied sciences or 3D-Integrated Circuits. This range varieties a motive force for the long run evolutions of embedded method designs methodologies.

The major expectancies from method designers’ standpoint are with regards to equipment, instruments and architectures aiding program complexity and layout cycle relief. complicated optimizations are necessary to meet layout constraints and to allow a large attractiveness of those new technologies.

Algorithm-Architecture Matching for sign and picture Processing provides a set of chosen contributions from either and academia, addressing varied features of Algorithm-Architecture Matching process starting from sensors to architectures layout. The scope of this ebook displays the range of power algorithms, together with sign, communique, photograph, video, 3D-Graphics applied onto a variety of architectures from FPGA to multiprocessor structures. numerous synthesis and source administration options leveraging layout optimizations also are defined and utilized to varied algorithms.

Algorithm-Architecture Matching for sign and picture Processing can 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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Additional 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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Toczek and S. Mancini Several image processing algorithms are using multi-resolution images to perform tasks such as vision [4], video compression [3] and 3D rendering [1]. Multiresolution images are used for vision algorithms to integrate some global information at the pixel level: the results at the low resolution are used to constrain the computations at the more detailed levels. To compress video, the motion estimation step also benefits from a multi-resolution pyramid of the input images: the coarse grain motion vectors at the low resolution are used to guide the computations at higher resolutions, preventing the algorithm to “fall” in some local minima.

Reference [8] reports simulation results of a cache designed for a volume rendering hardware architecture. For each sample, the pipeline fetches 8 data from the VoxelCache which is a full associative cache that contains blocs of voxels. The VoxelCache holds 512 lines each of 64 voxels to make a 32 KByte memory. The simulation results of [8] show a 90% pipeline utilization but the size of the rendered volume is limited to 1283 voxels because the VoxelCache is trashing when it cannot hold all the voxels along a line.

Barequet R, Feder M (1999) SICLIC: a simple inter-color lossless image coder. In: Proc data compression conf, pp 501–510 2. Benazza-Benyahia A, Pesquet J-C, Hamdi M (2002) Vector-lifting schemes for lossless coding and progressive archival of multispectral images. IEEE Trans Geosci Remote Sens 40(9):2011–2024 3. Chen X, Canagarajah N, Nunez-Yanez JL, Vitulli R (2007) Hardware architecture for lossless image compression based on context-based modelling and arithmetic coding. In: Proc IEEE int system on chip conf, pp 251–254 4.

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