Enhancing Transformer Architecture: Techniques for Efficient Inference

This paper explores recent advancements in optimizing transformer architectures for efficient inference. We investigate various techniques including pruning, quantization, knowledge distillation, and architectural modifications. Our experimental results demonstrate that combining these approaches can reduce inference time by up to 74% while maintaining over 95% of the original performance. We also introduce a novel attention mechanism that dynamically allocates computational resources based on input complexity. Our implementation shows promise for edge device deployment where computational resources are constrained.