One must be very careful in utilizing the available on-chip memory space in embedded MpSoC architectures, which may be very challenging due to data sharing among processors. This paper proposes and evaluates an on-chi...
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ISBN:
(纸本)0780392647
One must be very careful in utilizing the available on-chip memory space in embedded MpSoC architectures, which may be very challenging due to data sharing among processors. This paper proposes and evaluates an on-chip memory space management strategy based on data compression. The proposed strategy first uses a compiler analysis that reveals the order in which different data blocks will be required by the application. After that, it builds an integer linear programming based representation of the on-chip memory space management problem, and solves it using a publicly-available integer linear programming tool. The solution gives the optimum order in which data blocks should be compressed and decompressed to minimize execution cycles or energy consumption under an on-chip memory capacity limit.
This paper presents a physically based model and formulation for industrial load management. The formulation utilizes integer linear programming techniques for minimizing electricity costs by scheduling the loads sati...
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This paper presents a physically based model and formulation for industrial load management. The formulation utilizes integer linear programming techniques for minimizing electricity costs by scheduling the loads satisfying the process, storage, and production constraints. The proposed strategy is evaluated by a case study for a typical flour mill with different load management options. The results show that significant reductions in peak electricity consumption are possible under time of use tariffs.
Deconvolution of relationships between bacterial artificial chromosome (BAC) clones and genes is a crucial step in the selective sequencing of regions of interest in a genome. It often includes combinatorial pooling o...
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Deconvolution of relationships between bacterial artificial chromosome (BAC) clones and genes is a crucial step in the selective sequencing of regions of interest in a genome. It often includes combinatorial pooling of unique probes obtained from the genes (unigenes), and screening of the BAC library using the pools in a hybridization experiment. Since several probes can hybridize to the same BAC, in order for the deconvolution to be achievable the pooling design has to be able to handle a large number of positives. As a consequence, smaller pools need to be designed, which in turn increases the number of hybridization experiments, possibly making the entire protocol unfeasible. We propose a new algorithm that is capable of producing high-accuracy deconvolution even in the presence of a weak pooling design, i.e. when pools are rather large. The algorithm compensates for the decrease of information in the hybridization data by taking advantage of a physical map of the BAC clones. We show that the right combination of combinatorial pooling and our algorithm not only dramatically reduces the number of pools required, but also successfully deconvolutes the BAC-gene relationships with almost perfect accuracy. Software is available on request from the first author.
The Multi-Criteria Test Suite Minimization (MCTSM) problem aims to remove redundant test cases, guided by adequacy criteria such as code coverage or fault detection capability. However, current techniques either exhib...
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The Multi-Criteria Test Suite Minimization (MCTSM) problem aims to remove redundant test cases, guided by adequacy criteria such as code coverage or fault detection capability. However, current techniques either exhibit a high loss of fault detection ability or face scalability challenges due to the NP-hard nature of the problem, which limits their practical utility. We propose TripRL, a novel technique that integrates traditional criteria such as statement coverage and fault detection ability with test coverage similarity into an integerlinear Program (ILP), to produce a diverse reduced test suite with high test effectiveness. TripRL leverages bipartite graph representation and its embedding for concise ILP formulation and combines ILP with effective reinforcement learning (RL) training. This combination renders large-scale test suite minimization more scalable and enhances test effectiveness. Our empirical evaluations demonstrate that TripRL’s runtime scales linearly with the magnitude of the MCTSM problem. Notably, for large test suites from the Defects4j dataset where existing approaches fail to provide solutions within a reasonable time frame, our technique consistently delivers solutions in less than 47 minutes. The reduced test suites produced by TripRL also maintain the original statement coverage and fault detection ability while having a higher potential to detect unknown faults.
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