ASPPLN: Accelerated Symbolic Probability Propagation in Logic Network
Authors: 
Weihua Xiao, Weikang QianAuthors Info & Claims
Weihua Xiao, Weikang QianAuthors Info & ClaimsArticle No.: 49, Pages 1 - 9
Abstract
Probability propagation is an important task used in logic network analysis, which propagates signal probabilities from its primary inputs to its primary outputs. It has many applications such as power estimation, reliability analysis, and error analysis for approximate circuits. Existing methods for the task can be divided into two categories: simulation-based and probability-based methods. However, most of them suffer from low accuracy or bad scalability. In this work, we propose ASPPLN, a method for accelerated symbolic probability propagation in logic network, which has a linear complexity with the network size. We first introduce a new definition in a graph called redundant input and take advantage of it to simplify the propagation process without losing accuracy. Then, a technique called symbol limitation is proposed to limit the complexity of each node's propagation according to the partial probability significances of the symbols. The experimental results showed that compared to the existing methods, ASPPLN improves the estimation accuracy of switching activity by up to 24.70%, while it also has a speedup of up to 29X.
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Published In
October 2022
1467 pages
ISBN:9781450392174
DOI:10.1145/3508352
- Conference Chair:
- Tulika Mitra,
- Program Chairs:
- Evangeline Young,
- Jinjun Xiong
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Published: 22 December 2022
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ICCAD '22
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ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design
October 30 - November 3, 2022
California, San Diego
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