TY - GEN
T1 - Close-to-optimal placement and routing for continuous-flow microfluidic biochips
AU - Grimmer, Andreas
AU - Wang, Qin
AU - Yao, Hailong
AU - Ho, Tsung Yi
AU - Wille, Robert
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/2/16
Y1 - 2017/2/16
N2 - Continuous-flow microfluidics rapidly evolved in the last decades as a solution to automate laboratory procedures in molecular biology and biochemistry. Therefore, the physical design of the corresponding chips, i.e., the placement and routing of the involved components and channels, received significant attention. Recently, several physical design solutions for this task have been presented. However, they often rely on general heuristics which traverse the search space in a rather arbitrary fashion and, additionally, consider placement and routing independently from each other. Consequently, the obtained results are often far from being optimal. In this work, a methodology is proposed which aims for determining close-to-optimal physical designs for continuous-flow microfluidic biochips. To this end, we consider all - or, at least, as much as possible - of the valid solutions. As this obviously yields a significant complexity, solving engines are utilized to efficiently traverse the search space and pruning schemes are proposed to reduce the search space without discarding too many promising solutions. Evaluations show that the proposed methodology is capable of determining optimal results for small experiments to be realized. For larger experiments, close-to-optimal results can efficiently be derived. Moreover, compared to the current state-of-the-art, improvements of up to 1-2 orders of magnitude can be observed.
AB - Continuous-flow microfluidics rapidly evolved in the last decades as a solution to automate laboratory procedures in molecular biology and biochemistry. Therefore, the physical design of the corresponding chips, i.e., the placement and routing of the involved components and channels, received significant attention. Recently, several physical design solutions for this task have been presented. However, they often rely on general heuristics which traverse the search space in a rather arbitrary fashion and, additionally, consider placement and routing independently from each other. Consequently, the obtained results are often far from being optimal. In this work, a methodology is proposed which aims for determining close-to-optimal physical designs for continuous-flow microfluidic biochips. To this end, we consider all - or, at least, as much as possible - of the valid solutions. As this obviously yields a significant complexity, solving engines are utilized to efficiently traverse the search space and pruning schemes are proposed to reduce the search space without discarding too many promising solutions. Evaluations show that the proposed methodology is capable of determining optimal results for small experiments to be realized. For larger experiments, close-to-optimal results can efficiently be derived. Moreover, compared to the current state-of-the-art, improvements of up to 1-2 orders of magnitude can be observed.
UR - https://www.scopus.com/pages/publications/85015294836
U2 - 10.1109/ASPDAC.2017.7858377
DO - 10.1109/ASPDAC.2017.7858377
M3 - Conference contribution
AN - SCOPUS:85015294836
T3 - Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC
SP - 530
EP - 535
BT - 2017 22nd Asia and South Pacific Design Automation Conference, ASP-DAC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd Asia and South Pacific Design Automation Conference, ASP-DAC 2017
Y2 - 16 January 2017 through 19 January 2017
ER -