Machine-learning-based models have recently gained traction as a way to overcome the slow downstream implementation process of FPGAs by building models that provide fast and accurate performance predictions. However, these models suffer from two main limitations: (1) training requires large amounts of data (features extracted from FPGA synthesis and implementation reports), which is cost-inefficient because of the time-consuming FPGA design cycle; (2) a model trained for a specific environment cannot predict for a new, unknown environment. In a cloud system, where getting access to platforms is typically costly, data collection for ML models can significantly increase the total cost-ownership (TCO) of a system. To overcome these limitations, we propose LEAPER, a transfer learning-based approach for FPGA-based systems that adapts an existing ML-based model to a new, unknown environment to provide fast and accurate performance and resource utilization predictions. Experimental results show that our approach delivers, on average, 85% accuracy when we use our transferred model for prediction in a cloud environment with 5-shot learning and reduces design-space exploration time by 10x, from days to only a few hours.

LEAPER是一种基于转移学习的方法，用于预测FPGA系统中的性能和资源使用率，通过将低端边缘环境训练的性能和资源使用率模型转移到新的高端云环境来提供快速准确的预测。实验结果表明，LEAPER在云环境中使用传输模型进行预测时，在六种工作负载和五个FPGA的情况下，平均准确率为85％，并且将FPGA加速器实现的设计空间探索时间从几天缩短为几个小时。

LEAPER: 基于FPGA的系统性能快速准确预测通过迁移学习