A global router based on a multicommodity flow model
Integration, the VLSI Journal
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DAC '97 Proceedings of the 34th annual Design Automation Conference
Effective iterative techniques for fingerprinting design IP
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Algorithms for VLSI Physical Design Automation
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An Introduction to VLSI Physical Design
Multilevel optimization for large-scale circuit placement
Proceedings of the 2000 IEEE/ACM international conference on Computer-aided design
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Proceedings of the 2000 IEEE/ACM international conference on Computer-aided design
Modeling, Analysis, and Self-Management of Electronic Textiles
IEEE Transactions on Computers
Lattice sensor networks: capacity limits, optimal routing and robustness to failures
Proceedings of the 3rd international symposium on Information processing in sensor networks
Energy-Aware Routing for E-Textile Applications
Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Efficient and effective placement for very large circuits
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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This article presents an efficient technique for placement and routing of sensors/actuators and processing units in a grid network. The driver application that we present is a medical jacket, which requires an extremely high level of robustness and fault tolerance. The power consumption of such jacket is another key technological constraint. Our proposed interconnection network is a mesh of wires. A jacket made of fabric and wires would be susceptible to accidental damage via tears. By modeling the tears, we evaluate the probability of having failures on every segment of wires in our mesh interconnection network. Then, we study two problems of placement and routing in the sensor networks such that the fault tolerance is maximized while the power consumption is minimized. We develop efficient integer linear programming (ILP) formulations to address these problems and perform both placement and routing, simultaneously. This ensures that the solution is a lower bound for both problems. We evaluate the effectiveness of our proposed techniques on a variety of benchmarks.