Abstract-The design of embedded vision systems carries a difficult challenge regarding the access times of memories holding image data for some particular cases of image treatments. This paper studies the optimization challenge reflecting the efficient operation of adhoc memory systems proposed by electronic designers to alleviate this problem. New algorithms are proposed for producing solutions to this 3-objective problem, and numerical experiments are conducted on real-world data for validating their efficiency.
We consider the classical One-Dimensional Bin Packing Problem (1D-BPP), an N P-hard optimization problem, where, a set of weighted items has to be packed into one or more identical capacitated bins. We give an experimental study on using symmetry breaking constraints for strengthening the classical integer linear programming proposed to optimally solve this problem. Our computational experiments are conducted on the data-sets found in BPPLib and the results have confirmed the theoretical results.
In this article, we address the classical One-Dimensional Bin Packing Problem (1D-BPP), an N P-hard combinatorial optimization problem. We propose a new formulation of integer linear programming for the problem, which reduces the search space compared to those described in the literature, as well as two families of cutting planes. Computational experiments are conducted on the data-set found in BPPLib and the results show that it is possible to solve more instances and to decrease the computation time by using our new formulation.
The design of embedded vision systems, in confronting the "Memory Wall", exhibits many challenges, regarding for example design cost, energy consumption and performance. This paper considers a variant of the Job Shop Scheduling Problem with tooling constraints, arising in this context, in which the completion time (makespan) is to be minimized. This objective corresponds to the performance of the produced circuit.Given a set of tasks and a set of prerequisites, this class of problem aims to schedule all the tasks. Each task can be processed if all its prerequisites (a specific subset of prerequisites) are loaded in the buffers and stay available during its whole operation.We discuss different formulations using integer linear programming and point out its characteristics, namely the size and the quality of the linear programming relaxation bound. To solve this scheduling problem with large size, we compare three sets of approaches including a Constraint Programming, two constructive greedy heuristics (published in previous work), two models of LocalSolver, a Simulated Annealing algorithm and Beam Search algorithm. Numerical experiments are conducted on 16 benchmark instances from the literature as well as on 12 real-life non-linear image processing kernels for validating their efficiency.
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