Abstract
Recently, Transformer has achieved state-of-the-arts results in several research areas such as Natural Language Processing and Computer Vision. Due to Transformer has a very large number of parameters and its core module Multi-Head Attention has a complex structure, the optimization of Multi-Head Attention for Transformer is now the research hotspots. However, most of the current work focused on software model optimization or hardware accelerator design, but unilateral optimization from algorithms or hardware is difficult to give full play to comprehensive performance of Multi-Head Attention, which is not well adapted to its characteristics. To solve the above problem, we propose a Software and Hardware Fusion Multi-Head Attention structure, which has less inference latency with tiny accuracy loss than the existing software optimization methods and hardware accelerators. We implement this design on Xilinx ZCU102 and validate this model accuracy and inference time using CIFAR-10 dataset, and obtained accuracy within 1% loss with respect to the baseline, and inference time 15.19 times of the baseline.
This paper is supported by the National Natural Science Foundation of China under Grant No. 61972293.
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Hu, W., Xu, D., Liu, F., Fan, Z. (2022). Software and Hardware Fusion Multi-Head Attention. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13370. Springer, Cham. https://doi.org/10.1007/978-3-031-10989-8_51
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