Cooling schedules for optimal annealing
Mathematics of Operations Research
Simulated annealing for VLSI design
Simulated annealing for VLSI design
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
The annealing algorithm
RISA: accurate and efficient placement routability modeling
ICCAD '94 Proceedings of the 1994 IEEE/ACM international conference on Computer-aided design
A new algorithm for floorplan design
DAC '86 Proceedings of the 23rd ACM/IEEE Design Automation Conference
Algorithms for VLSI Physcial Design Automation
Algorithms for VLSI Physcial Design Automation
VPR: A new packing, placement and routing tool for FPGA research
FPL '97 Proceedings of the 7th International Workshop on Field-Programmable Logic and Applications
FPL '99 Proceedings of the 9th International Workshop on Field-Programmable Logic and Applications
A class of min-cut placement algorithms
DAC '77 Proceedings of the 14th Design Automation Conference
Adaptive FPGA Placement by Natural Optimization
RSP '00 Proceedings of the 11th IEEE International Workshop on Rapid System Prototyping (RSP 2000)
RSR: A New Rectilinear Steiner Minimum Tree Approximation for FPGA Placement and Global Routing
EUROMICRO '98 Proceedings of the 24th Conference on EUROMICRO - Volume 1
Effect of the prefabricated routing track distribution on FPGA area-efficiency
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
FPGA placement using space-filling curves: Theory meets practice
ACM Transactions on Embedded Computing Systems (TECS)
Improving simulated annealing-based FPGA placement with directed moves
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A fast discrete placement algorithm for FPGAs
Proceedings of the ACM/SIGDA international symposium on Field Programmable Gate Arrays
| Hi-index | 0.00 |
Placement is key issue of integrated circuit physical design. There exist some techniques inspired in thermodynamics coping with this problem as Simulated Annealing. In this article, we present a combinatorial optimization method directly derived from both Thermodynamics and Information Theory. In TCO (Thermodynamic Combinatorial Optimization), two kinds of processes are considered: microstate and macrostate transformations. Applying the Shannon's definition of entropy to reversible microstate transformations, a probability of acceptance based on Fermi--Dirac statistics is derived. On the other hand, applying thermodynamic laws to macrostate transformations, an efficient annealing schedule is provided. TCO has been compared with a custom Simulated Annealing (SA) tool on a set of benchmark circuits for the FPGA (Field Programmable Gate Arrays) placement problem. TCO has provided the high-quality results of SA, while inheriting the adaptive properties of Natural Optimization (NO).