A Comparison of Computing Spectral Transforms of Logic Functions using Python Frameworks on GPU

The spectral transforms of logic functions have numerous applications in logic synthesis, signal processing, pattern recognition, and in many related areas. It is very important to be able to efficiently compute these transforms. Python frameworks show extremely high computation performance for (Fast Fourier Transform) FFT -based algorithms executed on graphics processing units (GPUs). Thus, this paper presents a comparison of computation time for different implementations of spectral transform of logic functions, performed on GPUs using three different Python frameworks. It is used TensorFlow, PyTorch, and CuPy, frameworks. The experiments were performed on Nvidia GeForce RTX 2060 GPU, which belongs to the middle GPU price range. Computational times are compared using randomly-generated truth vectors of size up to 4096. The aim of this paper is to identify the computing platform and Python programming framework which produces the fastest spectral transform of logic functions.