Abstract
Continuous-Flow Microfluidic Biochip (CFMB), with their integrated features, bring traditional biochemical experiments on a single chip to accomplish complex operations and reactions through precise control, efficient reactions and emerging ways of saving reagents. In the field of intelligent digital healthcare, CFMB have attracted a lot of attention. However, traditional manual design schemes can no longer meet the needs of increasingly complex chip architecture design. Therefore, this paper proposes an automated design method for resource binding and module placement of CFMB based on a list scheduling algorithm and an improved Simulated Annealing algorithm. Through the resource binding and scheduling design based on the list scheduling algorithm, an effective scheduling strategy is generated, which effectively improves the biochip execution efficiency. In addition, the improved Simulated Annealing algorithm solves the module placement problem in the biochip in a limited physical space. Compared with some benchmark algorithms, the experimental results demonstrate the effectiveness of the method in the biochip design process and provide a practical framework for further development of CFMB in the field of intelligent digital healthcare.
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Acknowledgements
This work is supported by the fund of Fujian Province Digital Economy Alliance, the National Natural Science Foundation of China (No. U1905211), and the Natural Science Foundation of Fujian Province (No. 2020J01500).
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Yang, Z., Huang, H., Liu, Z., Dong, C., Xu, L. (2024). Resource Binding and Module Placement Algorithms for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare. In: Jin, H., Yu, Z., Yu, C., Zhou, X., Lu, Z., Song, X. (eds) Green, Pervasive, and Cloud Computing. GPC 2023. Lecture Notes in Computer Science, vol 14504. Springer, Singapore. https://doi.org/10.1007/978-981-99-9896-8_18
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DOI: https://doi.org/10.1007/978-981-99-9896-8_18
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