Transformer Compression

Transformer compression focuses on reducing the computational cost and memory footprint of large transformer models, crucial for deploying these powerful models on resource-constrained devices. Current research emphasizes techniques like pruning (including variational information bottleneck-based methods), quantization, knowledge distillation, and efficient architecture design, often applied to models such as BERT, RoBERTa, and GPT-2, as well as specialized architectures for tasks like speech recognition and license plate recognition. These efforts aim to improve the efficiency and accessibility of transformer models, impacting various fields from natural language processing and computer vision to molecular modeling and commercial applications. Significant advancements are being made in achieving high compression ratios with minimal performance degradation.