Performance Estimation Techniques With MPSoC Transaction-Accurate Models

De, Ma; Yan, Rong; Huang, Kai; Yu, Min; Xiu, Siwen; Hai-tong, GE; Yan, Xiaolang; Jerraya, Ahmed

doi:10.1109/tcad.2013.2275252

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…For example, software optimization requires time-consumption estimation for each software stage [Tu et al 2014], yield optimization for embedded systems-on-chip requires time consumption information for each resource [Meyer et al 2014], and communication system design and optimization requires message response time [Schneider et al 2014]. Moreover, for real-time embedded systems, accurate estimates of completion times are preferred compared to simple worst-case execution times [Ding et al 2012;Ma et al 2013]. Traditionally, predesigned rule-based specifications or even human judgment were sufficient for this purpose.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Optimization of Order Admission Policies in a Digital Print Factory

Duan

Zeng

Chakrabarty

et al. 2015

ACM Trans. Des. Autom. Electron. Syst.

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On-demand digital print service is an example of a real-time embedded enterprise system. It offers mass customization and exemplifies personalized manufacturing services. Once a print order is submitted to the print factory by a client, the print service provider (PSP) needs to make a real-time decision on whether to accept or refuse this order. Based on the print factory's current capacity and the order's properties and requirements, an order is refused if its acceptance is not profitable for the PSP. The order is accepted with the most appropriate due date in order to maximize the profit that can result from this order. We have developed an automated learning-based order admission framework that can be embedded into an enterprise environment to provide real-time admission decisions for new orders. The framework consists of three classifiers: Support Vector Machine (SVM), Decision Tree (DT), and Bayesian Probabilistic Model (BPM). The classifiers are trained by history orders and used to predict completion status for new orders. A decision integration technique is implemented to combine the results of the classifiers and predict due dates. Experimental results derived using real factory data from a leading print service provider and Weka open-source software show that the order completion status prediction accuracy is significantly improved by the decision integration strategy. The proposed multiclassifier model also outperforms a standalone regression model.

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