On embedding binary trees into hypercubes
Journal of Parallel and Distributed Computing
Embedding of Complete Binary Trees into Meshes with Row-Column Routing
IEEE Transactions on Parallel and Distributed Systems
A custom FPGA for the simulation of gene regulatory networks
Proceedings of the 13th ACM Great Lakes symposium on VLSI
Architecture-aware FPGA placement using metric embedding
Proceedings of the 43rd annual Design Automation Conference
FPGA placement using space-filling curves: Theory meets practice
ACM Transactions on Embedded Computing Systems (TECS)
The RLOC is dead - long live the RLOC
Proceedings of the 19th ACM/SIGDA international symposium on Field programmable gate arrays
Model-Based Closed-Loop Testing of Implantable Pacemakers
ICCPS '11 Proceedings of the 2011 IEEE/ACM Second International Conference on Cyber-Physical Systems
Digital mockups for the testing of a medical ventilator
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
A Custom FPGA Processor for Physical Model Ordinary Differential Equation Solving
IEEE Embedded Systems Letters
Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
Exploration with upgradeable models using statistical methods for physical model emulation
Proceedings of the 50th Annual Design Automation Conference
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Physical models utilize mathematical equations to model physical systems like airway mechanics, neuron networks, or chemical reactions. Previous work has shown that physical models can execute fast on FPGAs (field-programmable gate arrays). We introduce an approach for implementing physical models on FPGAs that applies graph theoretic techniques to make use of a physical model's natural structure--tree, ring, chain, etc.--resulting in model execution speedups. A first phase of the approach maps physical model equations to a structured virtual PE (processing element) graph using graph theoretic folding techniques. A second phase maps the structured virtual PE graph to physical PE regions on an FPGA using graph embedding theory. We also present a simulated annealing approach with custom cost and neighbor functions that can map any physical model onto an FPGA with low wire costs. Average circuit speedup improvements over previous works for various physical models are 65% using the graph embedding and 35% using the simulated annealing approach. Each approach's more efficient use of FPGA resources also enables larger models to be implemented on an FPGA device.