The staggering innovations and emergence of numerous deep learning (DL) applications
have forced researchers to reconsider hardware architecture to accommodate fast and …

During the last two years, the goal of many researchers has been to squeeze the last bit of
performance out of HPC system for AI tasks. Often this discussion is held in the context of …

Transformer structure, stacked by a sequence of encoder and decoder network layers,
achieves significant development in neural machine translation. However, vanilla …

Recurrent neural network (RNN) models have been found to be well suited for processing
temporal data. In this work, we present an optimized implementation of vanilla RNN cell and …

Currently, TensorFlow architecture only supports CPU and GPU programming, and has not
yet formed a unified support standard for FPGAs. To the best of our knowledge, when …

In the context of cross-cultural communication, translation between languages has become
increasingly important. Based on automatic Chinese–English translation, this study …

With the rise of artificial intelligence and machine learning, many applications and services
require FPGA support to speed up the training process and improve efficiency. FPGA has its …