Motivated by the growing demand for reduced-precision arithmetic in computational science, we exploit lower-precision emulation in Python{--}widely regarded as the dominant programming language for numerical analysis and machine learning. Low-precision paradigms have revolutionized deep learning by enabling more efficient computation and reduced memory footprint while maintaining model fidelity. To better enable numerical experimentation with and exploration of reduced-precision computation, we developed \texttt{pychop}, which supports customizable floating-point formats and a comprehensive set of rounding modes in Python, allowing users to benefit from fast, reduced-precision emulation in numerous applications. \texttt{pychop} also provides flexible interfaces for array and tensor backends, enabling efficient reduced-precision emulation on both CPUs and GPUs for neural network deployment. In this paper, we offer a comprehensive exposition of the design and applications of \texttt{pychop}. Furthermore, we present empirical results on reduced-precision emulation for image classification and object detection using published datasets, illustrating the sensitivity to low precision and delivering valuable insights into its quantization-aware training and post-quantization impacts. Establishing itself as a foundational tool for advancing mixed-precision algorithms, \texttt{pychop} enables in-depth investigations into the effects of numerical precision in scientific computing and deep learning deployment, facilitating the development of novel hardware accelerators.

翻译：暂无翻译