Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
92 tokens/sec
Gemini 2.5 Pro Premium
52 tokens/sec
GPT-5 Medium
25 tokens/sec
GPT-5 High Premium
22 tokens/sec
GPT-4o
99 tokens/sec
DeepSeek R1 via Azure Premium
87 tokens/sec
GPT OSS 120B via Groq Premium
457 tokens/sec
Kimi K2 via Groq Premium
252 tokens/sec
2000 character limit reached

A Canonical-based NPN Boolean Matching Algorithm Utilizing Boolean Difference and Cofactor Signature (1711.03269v1)

Published 9 Nov 2017 in cs.LO

Abstract: This paper presents a new compact canonical-based algorithm to solve the problem of single-output completely specified NPN Boolean matching. We propose a new signature vector Boolean difference and cofactor (DC) signature vector. Our algorithm utilizes the Boolean difference, cofactor signature and symmetry properties to search for canonical transformations. The use of symmetry and Boolean difference notably reduces the search space and speeds up the Boolean matching process compared to the algorithm proposed in [1]. We tested our algorithm on a large number of circuits. The experimental results showed that the average runtime of our algorithm 37% higher and its average search space 67% smaller compared to [1] when tested on general circuits.

Citations (4)

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube