This paper deals with the Two-Dimensional Cutting Stock Problem with Setup Cost (2CSP-S). This problem is composed of three optimization sub-problems: a 2-D Bin Packing (2BP) problem (to place images on patterns), a Linear Programming (LP) problem (to find for each pattern the number of stock sheets to be printed) and a combinatorial problem (to find the number of each image on each pattern). In this article, we solve the 2CSP-S focusing on this third sub-problem. A genetic algorithm was developed to automatically find the proper number of each image on patterns. It is important to notice that our approach is not a new packing technique. This work was conducted for a paper industry company and experiments were realized on real and artificial datasets.
We present techniques used to create a high performance application-specific instruction-set processor (ASIP) implementation of the Pattern-Based Directional Interpolation (PBDI) intra-field deinterlacing algorithm. The proposed techniques focus primarily on an efficient utilization of the available memory bandwidth. They include the use of Very Long Instruction Words (VLIW) and an appropriate choice of custom instructions and application-specific registers in order to form a processing pipeline. We report a speedup factor of 1351 in comparison with a software-only implementation of the algorithm running on a general-purpose 32-bit RISC processor.
This paper presents a systematic approach to the design of application-specific instruction-set processors for high speed computation of local neighborhood functions and intra-field deinterlacing. The intended application is real-time processing of high definition video. The approach aims at an efficient utilization of the available memory bandwidth by fully exploiting the data parallelism inherent to the target algorithm class. An appropriate choice of custom instructions and application-specific registers is used together with a very long instruction word architecture in order to mimic a pipelined systolic array. This leads to a processing speed close to the limit imposed by memory bandwidth constraints. For three intra-field deinterlacing algorithms and 2-D convolution with four kernel sizes, the design approach yields speedup factors between 36 and 1330, Area-Time (AT) product improvements between 12 and 243 , and energy consumption reduction factors between 13 and 262.
This paper deals with the two-dimensional cutting stock problem with set-up cost (2CSP-S). This problem is composed of three optimisation sub-problems: a 2-D bin packing (2BP) problem (to place images on patterns), a linear programming (LP) problem (to find for each pattern the number of stock sheets to be printed) and a combinatorial problem (to find the number of each image on each pattern). We have already developed two different metaheuristics to solve the 2CSP-S focussing on this third sub-problem: a simulated annealing and a genetic algorithm. In this article, we propose to compare these two approaches. It is important to notice that our approaches are not new packing techniques. This work was conducted for a paper industry company and experiments were realised on real and artificial data sets.
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