5 research outputs found
Fast Exact NPN Classification with Influence-aided Canonical Form
NPN classification has many applications in the synthesis and verification of
digital circuits. The canonical-form-based method is the most common approach,
designing a canonical form as representative for the NPN equivalence class
first and then computing the transformation function according to the canonical
form. Most works use variable symmetries and several signatures, mainly based
on the cofactor, to simplify the canonical form construction and computation.
This paper describes a novel canonical form and its computation algorithm by
introducing Boolean influence to NPN classification, which is a basic concept
in analysis of Boolean functions. We show that influence is
input-negation-independent, input-permutation-dependent, and has other
structural information than previous signatures for NPN classification.
Therefore, it is a significant ingredient in speeding up NPN classification.
Experimental results prove that influence plays an important role in reducing
the transformation enumeration in computing the canonical form. Compared with
the state-of-the-art algorithm implemented in ABC, our influence-aided
canonical form for exact NPN classification gains up to 5.5x speedup.Comment: To be appeared in ICCAD'2
Fast Adjustable NPN Classification Using Generalized Symmetries
NPN classification of Boolean functions is a powerful technique used in many logic synthesis and technology mapping tools in FPGA design flows. Computing the canonical form of a function is the most common approach of Boolean function classification. In this paper, a novel algorithm for computing NPN canonical form is proposed. By exploiting symmetries under different phase assignments and higher-order symmetries of Boolean functions, the search space of NPN canonical form computation is pruned and the runtime is dramatically reduced. The algorithm can be adjusted to be a slow exact algorithm or a fast heuristic algorithm with lower quality. For exact classification, the proposed algorithm achieves a 30× speedup compared to a state-of-the-art algorithm. For heuristic classification, the proposed algorithm has similar performance as the state-of-the-art algorithm with a possibility to trade runtime for quality
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum