Introduction: The dataset contains synthetically generated boolean functions in PLA file format that can be used for testing runtime and QoR of logic minimization approaches (e.g Espresso, BOOM, MinBLoG etc.) The size of the functions ranges from 100 to 400 variables and 200 to 1000 cubes. The dataset is divided into three different types of boolean functions: F-Type: Functions specified using their ON-Set. Contained in synth_bench_f.zip. FR-Type: Functions specified using their ON-Set and OFF-Set. Contained in synth_bench_fr.zip. FD-Type: Functions specified using their ON-Set and DontCare-Set (DC-Set). Contained in synth_bench_fd.zip. File Naming Convention: The filenames of the PLA files indicate the size of the function it contain. Example: fd_200_400-0.pla = Contains an FD type function with 200 variables and 400 cubes. The suffix 0 at the end indicates it is the first of the 5 random functions generated with 200 variables and 400 cubes. Reference: Please cite the work below when using this benchmark. The dataset was generated for testing the boolean logic minimization tool MinBLoG published in this work. Prianka Sengupta, Aakash Tyagi, Jiang Hu, Vivek K Rajan, Hesham Mostafa, and Somdeb Majumdar. 2024. MinBLoG: Minimization of Boolean Logic Functions using Graph Attention Network. In 2024 ACM/IEEE International Symposium on Machine Learning for CAD (MLCAD ’24), September 9–11, 2024, Salt Lake City, UT, USA. ACM, New York, NY, USA, 8 pages. https: //doi.org/10.1145/3670474.3685962 For the most up-to-date version of the dataset and accompanying tools, please check the GitHub repository below: https://github.com/puprianka/minblog