👉 Forced computing refers to a computational approach where the system is compelled or constrained to perform specific tasks or algorithms, often to achieve desired outcomes or optimize performance under certain conditions. This can involve setting up hardware or software environments that enforce particular computational paths, such as using specialized accelerators for specific tasks like matrix multiplication or neural network inference. In machine learning, forced computing might be used to enforce constraints during training, such as regularization techniques that penalize certain types of model behavior or to ensure that the model adheres to specific fairness criteria. This method is crucial in scenarios where the goal is to enhance efficiency, ensure compliance with regulations, or achieve specific performance metrics that are critical for the application at hand.
