This study addresses the deployment challenges of integer-only quantized Transformers on resource-constrained embedded FPGAs (Xilinx Spartan-7 XC7S15). We enhanced the flexibility of our VHDL template by introducing a selectable resource type for storing intermediate results across model layers, thereby breaking the deployment bottleneck by utilizing BRAM efficiently. Moreover, we developed a resource-aware mixed-precision quantization approach that enables researchers to explore hardware-level quantization strategies without requiring extensive expertise in Neural Architecture Search. This method provides accurate resource utilization estimates with a precision discrepancy as low as 3%, compared to actual deployment metrics. Compared to previous work, our approach has successfully facilitated the deployment of model configurations utilizing mixed-precision quantization, thus overcoming the limitations inherent in five previously non-deployable configurations with uniform quantization bitwidths. Consequently, this research enhances the applicability of Transformers in embedded systems, facilitating a broader range of Transformer-powered applications on edge devices.

翻译：本研究针对整数量化Transformer在资源受限的嵌入式FPGA（Xilinx Spartan-7 XC7S15）上的部署挑战展开。我们通过引入可选择的资源类型来存储模型层间的中间结果，增强了VHDL模板的灵活性，从而通过高效利用BRAM突破了部署瓶颈。此外，我们开发了一种资源感知的混合精度量化方法，使研究人员能够在无需神经架构搜索专业知识的情况下探索硬件级量化策略。该方法提供的资源利用率预估精度与实际部署指标差异可低至3%。与先前工作相比，我们的方法成功实现了采用混合精度量化的模型配置部署，从而克服了先前五种采用均匀量化位宽配置无法部署的固有局限。因此，本研究提升了Transformer在嵌入式系统中的适用性，为边缘设备上更广泛的Transformer驱动应用创造了条件。