LEAPER: Modeling Cloud FPGA-based Systems via Transfer Learning
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: Modeling Cloud FPGA-based Systems via Transfer Learning
G. Singh, D. Diamantopoulos, J. Gomez-Luna, S. Stuijk, H. Corporaal, O. Mutlu.
In 40th International Conference of Computer Design, ICCD 2022, Proceedings, pages xyz-xyz. Lake Tahoe, USA, 23-26 October 2022. IEEE Computer Society Press, Los Alamitos, CA, USA, 2022. (abstract, pdf, doi).