👉 Removed computing refers to the omission of certain computational steps or processes from a model's architecture or training procedure, typically to simplify the model, reduce computational costs, or improve efficiency. This can involve removing layers, using fewer parameters, or applying techniques like pruning or quantization. The goal is to maintain or even enhance the model's performance while making it more accessible for deployment on resource-constrained devices or reducing training time and energy consumption.
