An Efficient Method of Computing Generalized Reed-Muller Expansions from Binary Decision Diagram
IEEE Transactions on Computers
Spectral transforms for large boolean functions with applications to technology mapping
DAC '93 Proceedings of the 30th international Design Automation Conference
Synthesis by spectral translation using Boolean decision diagrams
DAC '96 Proceedings of the 33rd annual Design Automation Conference
Boolean Functions Classification via Fixed Polarity Reed-Muller Forms
IEEE Transactions on Computers
Boolean function representation and spectral characterization using AND/OR graphs
Integration, the VLSI Journal
Decision Diagram Method for Calculation of Pruned Walsh Transform
IEEE Transactions on Computers
Spectral decision diagrams using graph transformations
Proceedings of the conference on Design, automation and test in Europe
Spectral Techniques in VLSI CAD
Spectral Techniques in VLSI CAD
Spectral Techniques in Digital Logic
Spectral Techniques in Digital Logic
Use of the Autocorrelation Function in the Classification of Switching Functions
DSD '02 Proceedings of the Euromicro Symposium on Digital Systems Design
Computation of Spectral Information from Logic Netlists
ISMVL '00 Proceedings of the 30th IEEE International Symposium on Multiple-Valued Logic
Autocorrelation coefficients in the representation and classification of switching functions
Autocorrelation coefficients in the representation and classification of switching functions
Mixed-radix MVL Function Spectral and Decision Diagram Representation
Automation and Remote Control
Classification of Boolean functions by the invariants of their matrix representation
Automation and Remote Control
IEEE Transactions on Computers
Efficient calculation of spectral coefficients and their applications
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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This paper presents some new considerations for spectral techniques for classification of Boolean functions. These considerations incorporate discussions of the feasibility of extending this classification technique beyond n = 5. A new implementation is presented along with a basic analysis of the complexity of the problem. We also note a correction to results in this area that were reported in previous work.