Lightweight Transformer

Lightweight Transformers aim to reduce the computational cost and memory footprint of standard Transformer architectures while maintaining performance comparable to their larger counterparts. Current research focuses on developing efficient model variations, such as those incorporating sparse attention mechanisms, hybrid CNN-Transformer designs, and unrolled optimization algorithms, often applied to tasks like image processing, natural language processing, and time-series analysis. This pursuit of efficiency is crucial for deploying Transformer models on resource-constrained devices and expanding their applicability across diverse fields, including medical imaging, mobile computing, and embedded systems.