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

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

Advances in sign and snapshot processing including expanding computing strength are bringing cellular expertise towards purposes in various domain names like car, well-being, telecommunication, multimedia, leisure and so forth. the improvement of those best functions, concerning a wide range of algorithms (e.g. sign, photo, video, 3D, verbal exchange, cryptography) is classically divided into 3 consecutive steps: a theoretical learn of the algorithms, a learn of the objective structure, and eventually the implementation. the sort of linear layout stream is attaining its limits as a result of extreme strain on layout cycle and strict functionality constraints. The method, known as Algorithm-Architecture Matching, goals to leverage layout flows with a simultaneous research 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 necessary while dealing with the hot rising functions as for instance complicated cellular verbal exchange or photographs utilizing sub-micron production applied sciences or 3D-Integrated Circuits. This range types a driver for the long run evolutions of embedded procedure designs methodologies.

The major expectancies from process designers’ perspective are with regards to equipment, instruments and architectures helping program complexity and layout cycle aid. complicated optimizations are necessary to meet layout constraints and to permit a large recognition of those new technologies.

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

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

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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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Rays exiting the scene are counted back but not inserted in the “Index RAM”. 3A ray phase is the ray coordinate along the phase axis. 42 T. Toczek and S. Mancini Fig. 9 Architecture of the two main elements of the recursive grid traversal pipeline Fig. 10 In some circumstances, diving may out-phase some of the rays of a beam, while keeping others in-phase (gray voxels are on the beam phase) Also, the phase synchronization have to be performed on all the levels of resolution. So, the “Index RAM” in-phase and out-of-phase parts are again divided in sub-parts for each level of resolution.

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. The background memory is 64 bit wide and a word contains a 2 × 2 × 2 part of the volume.

The efficient management of multi-resolution and recursive data structure is one of the challenge to design embedded systems for image processing. Multi-resolution 3D grids are widely used for applications such as realistic rendering ray-tracing, medical visualization, volume rendering and tomographic reconstruction. These applications extensively use the ray shooting kernel to simulate the propagation of light in a 3D volume. Ray shooting based algorithms are widely considered as both computationally and bandwidth hungry.

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