Abstract
This research work presents a methodology for debugging embedded designs by using recurrent neural networks. In this methodology, a cycle-accurate lossless debugging system with unlimited trace window is used for debugging. The lossless trace resembles a time data series. A recurrent neural network trained either through a golden reference or from the actual time series can be used to predict the incoming debugging data. A bug can be easily isolated based upon the discrepancy between the received and the predicted time series. This allows to draw conclusions to speed up the debugging process.
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This research has received funding under the grant number 100369691 by the German Federal State of Saxony.
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Khan, H., Podlubne, A., Akgün, G., Göhringer, D. (2020). Cycle-Accurate Debugging of Embedded Designs Using Recurrent Neural Networks. In: Rincón, F., Barba, J., So, H., Diniz, P., Caba, J. (eds) Applied Reconfigurable Computing. Architectures, Tools, and Applications. ARC 2020. Lecture Notes in Computer Science(), vol 12083. Springer, Cham. https://doi.org/10.1007/978-3-030-44534-8_6
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DOI: https://doi.org/10.1007/978-3-030-44534-8_6
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