Short proofs for tricky formulas
Acta Informatica
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Power minimization in IC design: principles and applications
ACM Transactions on Design Automation of Electronic Systems (TODAES)
HEAT: hierarchical energy analysis tool
DAC '96 Proceedings of the 33rd annual Design Automation Conference
Solving Boolean Equations Using ROSOP Forms
IEEE Transactions on Computers
Bounding Signal Probabilities for Testability Measurement Using Conditional Syndromes
IEEE Transactions on Computers
Solution of Switching Equations Based on a Tabular Algebra
IEEE Transactions on Computers
On Optimizing BIST-Architecture by Using OBDD-based Approaches and Genetic Algorithms
VTS '97 Proceedings of the 15th IEEE VLSI Test Symposium
OBDD-Based Optimization of Input Probabilities for Weighted Random Pattern Generation
FTCS '95 Proceedings of the Twenty-Fifth International Symposium on Fault-Tolerant Computing
GlitchMap: an FPGA technology mapper for low power considering glitches
Proceedings of the 44th annual Design Automation Conference
An efficient method to identify critical gates under circuit aging
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
FPGA-targeted high-level binding algorithm for power and area reduction with glitch-estimation
Proceedings of the 46th Annual Design Automation Conference
SETmap: a soft error tolerant mapping algorithm for FPGA designs with low power
Proceedings of the 16th Asia and South Pacific Design Automation Conference
| Hi-index | 14.99 |
The problem is presented in the context of some recent theoretical advances on a related problem, called random satisfiability. These recent results indicate the theoretical limitations inherent in the problem of computing signal probabilities. Such limitations exist even if one uses Monte Carlo techniques for estimating signal probabilities. Theoretical results indicate that any practical method devised to compute signal probabilities would have to be evaluated purely on an empirical basis. An improved algorithm is offered for estimating the signal probabilities that takes into account the first-order effects of reconvergent input leads. It is demonstrated that this algorithm is linear in the product of the size of the network and the number of inputs. Empirical evidence is given indicating the improved performance obtained using this method over the straightforward probability computations. The results are very good, and the algorithm is very fast and easy to implement.