  In the field of deep learning, the prevalence of models initially trained
with 32-bit precision is a testament to its robustness and accuracy. However,
the continuous evolution of these models often demands further training, which
can be resource-intensive. This study introduces a novel approach where we
continue the training of these pre-existing 32-bit models using 16-bit
precision. This technique not only caters to the need for efficiency in
computational resources but also significantly improves the speed of additional
training phases. By adopting 16-bit precision for ongoing training, we are able
to substantially decrease memory requirements and computational burden, thereby
accelerating the training process in a resource-limited setting. Our
experiments show that this method maintains the high standards of accuracy set
by the original 32-bit training while providing a much-needed boost in training
speed. This approach is especially pertinent in today's context, where most
models are initially trained in 32-bit and require periodic updates and
refinements. The findings from our research suggest that this strategy of
16-bit continuation training can be a key solution for sustainable and
efficient deep learning, offering a practical way to enhance pre-trained models
rapidly and in a resource-conscious manner.
