A high-efficient Swin Transformer accelerator design

This study used adaptive iterative pruning and search-based bias-free quantization approaches to minimize model complexity and storage requirements while preserving accuracy in response to the deployment and execution problems of Swin Transformer models in resource-constrained situations. Initially, Adaptive iterative pruning dynamically eliminated non-essential weights, reducing storage and computational demands. Subsequently, Search-based bias-Free quantization optimized the storage of weights and activation values, further minimizing model size with minimal accuracy loss. In terms of hardware design, an accelerator architecture tailored for Swin Transformer models was put out, improving data processing efficiency and speed via hardware-level adjustments. Experimental results indicated that after applying pruning and quantization, the model's size reduced to 14.4% of its original, achieving a Top-1 accuracy of 77.4% on the ImageNet-1K dataset.