Bi-Kronecker Functional Decision Diagrams: A Novel Canonical Representation of Boolean Functions
Authors: Xuanxiang Huang, Kehang Fang, Liangda Fang, Qingliang Chen, Zhao-Rong Lai, Linfeng Wei2867-2875
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results demonstrate that BKFDDs outperform other existing decision diagrams in terms of sizes. Experiments are carried out to compare BDDs, KFDDs, BBDDs and BKFDDs on the MCNC benchmarks. |
| Researcher Affiliation | Academia | Department of Computer Science, Jinan University Guangzhou 510632, China; Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, China. |
| Pseudocode | Yes | Algorithm 1: f h |
| Open Source Code | No | The paper states, 'We implement a package that integrates BKFDDs as well as BDDs and KFDDs using C programming language,' but does not provide a link or explicit statement that this implementation is open-source. A link for a *third-party* BBDD package is provided, but not for the authors' work. |
| Open Datasets | Yes | Experiments are carried out to compare BDDs, KFDDs, BBDDs and BKFDDs on the MCNC benchmarks2. (Footnote 2: https://ddd.fit.cvut.cz/prj/Benchmarks/) |
| Dataset Splits | No | The paper mentions using MCNC benchmarks but does not specify any training, validation, or test splits, nor does it refer to predefined splits with citations or provide any splitting methodology details. |
| Hardware Specification | Yes | The machine running the benchmark is equipped with an Intel Core i7-8086K 4GHz CPU and 32GB RAM. |
| Software Dependencies | No | The paper mentions using 'C programming language,' 'ABC tools,' and 'CUDD,' and a 'BBDD package' but does not provide specific version numbers for any of these software components. |
| Experiment Setup | No | The paper describes the 'compilation process' and 'reordering algorithm' but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed system-level training settings. |