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Dataset for Accelerating Hybrid XOR–CNF SAT Problems Natively with In-Memory Computing

This repository contains the instance files used for the paper "Accelerating Hybrid XOR–CNF SAT Problems Natively with In-Memory Computing." The datasets are organized based on their representation (CNF vs. XNF) and whether they have undergone preprocessing.&

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CreatorIm, Haesol
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Published2026-01-13
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DOI10.5281/zenodo.18235974
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Downloads27
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Licensecc-by-4.0
File Size196.1 KB
Data TypeDataset
Published2026
Licensecc-by-4.0
Total Views95
Total Downloads27

This repository contains the instance files used for the paper “Accelerating Hybrid XOR–CNF SAT Problems Natively with In-Memory Computing.” The datasets are organized based on their representation (CNF vs. XNF) and whether they have undergone preprocessing. 

 

Folder Structure Overview 

The instances are categorized into the following four folders:  

CNF: Contains original instances obtained from public repositories.  

CNF-PP: Contains instances preprocessed using the Processor module of the PySAT library (https://github.com/pysathq/pysat).  

XNF: Contains instances converted from the original CNF files into XOR+CNF format using the cnf2xnf tool (https://github.com/arminbiere/cnf2xnf).  

XNF-PP: Contains instances that were first preprocessed with PySAT and subsequently converted to XOR+CNF via cnf2xnf. 

 

Problem Categories  

Each of the folders above is further organized by the specific problem type:  

McEliece: Instances related to the McEliece cryptosystem.  

MDP: Instances originated from the Minimal Disagreement Problem.  

AES: Instances originated from the Advanced Encryption Standard (AES). 

 

Data Sources  

The raw instances located in the CNF folder were sourced directly from the following public repositories:  

McEliece: Generated using the tools available at https://github.com/nasa/PySA/tree/pysa-mceliece

AES: Accessed from <a href="https://www.cril.univ-artois.fr/SAT11/

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Dataset for Accelerating Hybrid XOR–CNF SAT Problems Natively… (Full Dataset)196.1 KB
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Files are hosted on the source repository. Click download to access the full dataset.

Im, Haesol (2026). Dataset for Accelerating Hybrid XOR–CNF SAT Problems Natively with In-Memory Computing. https://doi.org/10.5281/zenodo.18235974