Hardware Acceleration for Boolean Satisfiability Solver by Applying Belief Propagation Algorithm
March 16, 2016 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Te-Hsuan Chen, Ju-Yi Lu
arXiv ID
1603.05314
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.LO
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Boolean satisfiability (SAT) has an extensive application domain in computer science, especially in electronic design automation applications. Circuit synthesis, optimization, and verification problems can be solved by transforming original problems to SAT problems. However, the SAT problem is known as NP-complete, which means there is no efficient method to solve it. Therefore, an efficient SAT solver to enhance the performance is always desired. We propose a hardware acceleration method for SAT problems. By surveying the properties of SAT problems and the decoding of low-density parity-check (LDPC) codes, a special class of error-correcting codes, we discover that both of them are constraint satisfaction problems. The belief propagation algorithm has been successfully applied to the decoding of LDPC, and the corresponding decoder hardware designs are extensively studied. Therefore, we proposed a belief propagation based algorithm to solve SAT problems. With this algorithm, the SAT solver can be accelerated by hardware. A software simulator is implemented to verify the proposed algorithm and the performance improvement is estimated. Our experiment results show that time complexity does not increase with the size of SAT problems and the proposed method can achieve at least 30x speedup compared to MiniSat.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Artificial Intelligence
π
π
The Cartographer
R.I.P.
π»
Ghosted
Explanation in Artificial Intelligence: Insights from the Social Sciences
R.I.P.
π»
Ghosted
Federated Machine Learning: Concept and Applications
R.I.P.
π»
Ghosted
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
R.I.P.
π»
Ghosted
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
R.I.P.
π»
Ghosted
Rainbow: Combining Improvements in Deep Reinforcement Learning
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted