Vision Transformers (ViTs) have dramatically advanced computer vision performance, but challenges arise when deploying them on embedded devices due to high computational and memory demands. The paper ‘An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViT’ addresses this by proposing an FPGA-based accelerator, specifically designed for EfficientViT which combines Convolution and Transformer architectures.

• The design focuses on reconfigurability to support different operations efficiently.
• It introduces a time-multiplexed and pipelined dataflow to optimize data access.
• The proposed accelerator achieves a remarkable 780.2 GOPS throughput and 105.1 GOPS/W energy efficiency.
• It significantly outperforms previous works, setting new benchmarks for hardware efficiency in ViTs.

The significance of this study lies in its potential to bring powerful vision transformer models to resource-constrained environments. By boosting hardware utilization and efficiency, applications in real-time computing and edge devices become more viable, opening the door for a myriad of embedded vision applications.