The use of symbolic data structures to store pseudo-Boolean (i.e. integer-valued) functions has proved to be extremely effective in handling both transform matrices and spectral representations of large Boolean functions. The authors propose a novel application of symbolic spectral analysis techniques to the prediction of the complexity of a combinational circuit. They present a symbolic formulation of the problem, and propose an implicit algorithm for its solution that performs well, in terms of both execution time and accuracy in the computation, on circuits that are sensibly larger than the ones usually handled by the tools currently available. Experimental results, collected on standard examples, are discussed to support this claim
Predicting the Complexity of Large Combinational Circuits ThroughSymbolic Spectral Analysis of their Functional Specifications / Macii, Enrico; Poncino, M.. - In: IEE PROCEEDINGS. COMPUTERS AND DIGITAL TECHNIQUES. - ISSN 1350-2387. - 144:(1997), pp. 343-347. [10.1049/ip-cdt:19971366]
Predicting the Complexity of Large Combinational Circuits ThroughSymbolic Spectral Analysis of their Functional Specifications
MACII, Enrico;PONCINO M.
1997
Abstract
The use of symbolic data structures to store pseudo-Boolean (i.e. integer-valued) functions has proved to be extremely effective in handling both transform matrices and spectral representations of large Boolean functions. The authors propose a novel application of symbolic spectral analysis techniques to the prediction of the complexity of a combinational circuit. They present a symbolic formulation of the problem, and propose an implicit algorithm for its solution that performs well, in terms of both execution time and accuracy in the computation, on circuits that are sensibly larger than the ones usually handled by the tools currently available. Experimental results, collected on standard examples, are discussed to support this claimPubblicazioni consigliate
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https://hdl.handle.net/11583/1402062
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