## 2 Previous Work  
Recent end-to-end ASR models typically consist of an encoder and decoder, wherein a strong encoder architecture plays a vital role in achieving optimal ASR system performance. The Conformer architecture efficiently models both global and local dependencies by employing a convolutionally augmented transformer, and it becomes a practical model for ASR tasks as well as various speech processing tasks. Additionally, ref. [23] proposes a progressive downsampling scheme to reduce the training and inference costs of the model. Ref. [24] introduces a similar progressive downsampling scheme, but also includes an upsampling mechanism [25; 26] to improve the representational capability of the model. Meanwhile, ref. [27] utilizes an additional add-on downsampling module that fine-tunes the time-frequency speech features. However, the quadratic complexity of the attention layer remains excessively high at longer sequence lengths. Furthermore, downsampling approach reduces the training and inference costs of model, but temporal downsampling leads to unstable and divergent training, weakening the representational capabilities of model.  
The Transformer abandons the RNN and CNN commonly used in research and instead adopts the self-attention mechanism to encode the sequenced information, resulting in an improved translation performance and training efficiency of the NMT model. Several pre-training techniques [28; 29] have been proposed to train large models using large existing corpora. The pre-training and fine-tuning approach further improves the translation results of NMT. Replicating target data for constructing a pseudo-parallel corpus can yield positive results [30; 31] to improve the translation quality of low-resource machine translation. Nevertheless, the lack of corpus resources remains the most common problem of NMT. For Zhuang language, data augmentation and expansion as well as noise reduction of parallel corpus are required for low-resource scenarios.  
Transformer-based models are becoming larger and more computation-intensive, and existing FPGA acceleration attempts to overcome this problem in two ways, including data flow optimization and model optimization. Ref. [19] describes splitting large matrices in transformer and designing pulsation arrays based on computational flow, while also optimizing computation of nonlinear functions. Ref. [32] mentions reducing computation including approximation algorithms, along with leveraging parallelism and specializing data paths to optimize hardware utilization. In Ref. [33], the weights were converted to block circulant matrix form and FFT's multiplication method was applied instead of matrix-vector multiplication. FPGAs are high-speed, use significantly less power and cost less to realize system design requirements.  
Although ASR and NMT systems have gained significant attention in the field of artificial intelligence (AI), research on the neural network models of Zhuang is at a preliminary stage and existing literature is lacking. Currently, no practical cloud platform or natural language processing system has been developed for Zhuang language. Communicating with volunteers and marketing specialized products in remote mountainous regions can be challenging, particularly with villagers who only speak Zhuang language. This difficulty also extends to cultural exchange and transmission. An established resolution would be to explore the edge of the cloud platform system with remote speech recognition functions and multilingual translation.