Algorithm selection: a quantitative computation-intensive optimization approach
ICCAD '94 Proceedings of the 1994 IEEE/ACM international conference on Computer-aided design
Fast Prototyping of Datapath-Intensive Architectures
IEEE Design & Test
JSIM: A JAVA-Based Simulation and Animation Environment
SS '97 Proceedings of the 30th Annual Simulation Symposium (SS '97)
Sharing of SRAM tables among NPN-equivalent LUTs in SRAM-based FPGAs
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
ORION 2.0: a fast and accurate NoC power and area model for early-stage design space exploration
Proceedings of the Conference on Design, Automation and Test in Europe
Platune: a tuning framework for system-on-a-chip platforms
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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
Embedding-based placement of processing element networks on FPGAs for physical model simulation
Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
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Physical models capture environmental phenomena such as biochemical reactions, a beating heart, or neuron synapses, using mathematical equations. Previous work has shown that physical models can execute orders of magnitude faster on FPGAs (Field-Programmable Gate Arrays) compared to desktop PCs. Different models of the same physical phenomenon may vary, with "upgraded" models being more accurate but using more FPGA area and having slower performance. We propose that design space exploration considering upgradable models can dramatically increase the useful design space. We present an analysis of the solution space for utilizing networks of processing-elements (PEs) on FPGAs to emulate physical models, implement a web-based frontend to a compiler and cycle-accurate simulator of PE networks to estimate solution metrics, and utilize design-of-experiments (DOE) statistical methods to identify Pareto points. By considering upgradeable models during the design space exploration of a human lung physical model, the solution space of possible speedup, area, and accuracy is increased by 6X, 7.3X, and 1.5X, respectively, compared to evaluating a single model.