Optimizing heavy models with early exit branches

Everyday models get heavier and heavier (in terms of learnable parameters). For example, LEMON_large has 200M parameters and GPT-3 has over 175 billion parameters! Though they give State-of-the-Art Performance, how well are they deployed today? This calls for an efficient and faster method for training and inferring. So, we explore various methods through which we can speed up compute-intensive networks while preserving accuracy!