Title
Fast Adjustable NPN Classification using Generalized Symmetries.
Abstract
NPN classification of Boolean functions is a powerful technique used in many logic synthesis and technology mapping tools in both standard cell and FPGA design flows. Computing the canonical form is the most common approach of Boolean function classification. This article proposes two different hybrid NPN canonical forms and a new algorithm to compute them. By exploiting symmetries under different phase assignment as well as higher-order symmetries, the search space of NPN canonical form computation is pruned and the runtime is dramatically reduced. Nevertheless, the runtime for some difficult functions remains high. Fast heuristic method can be used for such functions to compute semi-canonical forms in a reasonable time. The proposed algorithm can be adjusted to be a slow exact algorithm or a fast heuristic algorithm with lower quality. For exact NPN classification, the proposed algorithm is 40× faster than state-of-the-art. For heuristic classification, the proposed algorithm has similar performance as state-of-the-art with a possibility to trade runtime for quality.
Year
DOI
Venue
2018
10.1109/FPL.2018.00008
FPL
Keywords
Field
DocType
Boolean matching, Boolean signatures, NPN classification, canonical form, symmetry
Boolean function,Logic synthesis,Heuristic,Exact algorithm,Computer science,Heuristic (computer science),Parallel computing,Algorithm,Canonical form,Statistical classification,Speedup
Conference
ISSN
ISBN
Citations 
1946-1488
978-1-5386-8518-1
0
PageRank 
References 
Authors
0.34
12
4
Name
Order
Citations
PageRank
Xuegong Zhou1416.28
Lingli Wang28625.42
Peiyi Zhao3969.81
Alan Mishchenko498284.79