Fast Transformer

Fast Transformer research focuses on accelerating the inference and training of large transformer models, addressing the computational bottleneck imposed by their quadratic complexity in sequence length. Current efforts concentrate on developing novel attention mechanisms (e.g., linear-time approximations, hierarchical approaches), optimized hardware implementations (FPGAs, GPUs), and model compression techniques (pruning, quantization, knowledge distillation) to improve speed and efficiency without significant accuracy loss. These advancements are crucial for deploying large language models in resource-constrained environments and enabling real-time applications across diverse fields, including natural language processing, computer vision, and high-energy physics.