Developed and integrated ZeroPadding1D and ZeroPadding2D layers for the fastmachinelearning/hls4ml repository, focusing on maintaining bit-exact precision in quantized deep learning models. The implementation ensured that padding operations did not compromise quantized inference accuracy, supporting reliable deployment in environments where precision is critical. Using Python and Keras, the work addressed the challenge of accurate padding propagation by aligning the new layers with the bit_exact pass, thereby preserving data integrity throughout the quantization process. The feature was delivered as a production-ready, well-tested addition, reflecting a targeted approach to enhancing quantization workflows without introducing regressions or compatibility issues.