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摘要
The Transformer model based on attention mechanism demonstrates superior performance. The complexity of the Transformer model includes both quantity and structural complexity, where the structural complexity leads to a mismatch between irregular models and regular hardware, reducing the efficiency of mapping the model to the hardware. Current accelerator research mainly focuses on addressing the complexity in terms of model quantity, but there is limited research on how to tackle the complexity in model structure. A regularized compressed model is proposd to reduce the structural complexity of the model, improving the matching between the model and the hardware, and increasing the efficiency of mapping the model to the hardware. A hardware-friendly model compression method is introduced, which utilizes a rule-based pruning scheme for weight with offset diagonals and simplifies the hardware quantization inference logic.An efficient and flexible hardware architecture is also present, including a pulsatile operation array with weight fixed at the block level, as well as a quasi-distributed storage architecture. This architecture enables efficient mapping of algorithms to the operation array, while achieving high data storage efficiency and reducing data movement. Experimental results show that the proposed approach achieves a compression rate of 93.75% with minimal performance loss. The accelerator implemented on an FPGA can efficiently handle the compressed Transformer model, resulting in energy efficiency improvements of 12.45 times compared to Central Processing Unit (CPU) and 4.17 times compared to Graphics Processing Unit (GPU).n energy efficiency improvements of 12.45 times compared to Central Processing Unit (CPU) and 4.17 times compared to Graphics Processing Unit (GPU). ? 2024 Science Press.
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