Machine learning has recently gained traction as a way to overcome the slow
accelerator generation and implementation process on an FPGA. It can be used to
build performance and resource usage models that enable fast early-stage design
space exploration. First, training requires large amounts of data (features
extracted from design synthesis and implementation tools), which is
cost-inefficient because of the time-consuming accelerator design and
implementation process. Second, a model trained for a specific environment
cannot predict performance or resource usage for a new, unknown environment. In
a cloud system, renting a platform for data collection to build an ML model can
significantly increase the total-cost-ownership (TCO) of a system. Third,
ML-based models trained using a limited number of samples are prone to
overfitting. To overcome these limitations, we propose LEAPER, a transfer
learning-based approach for prediction of performance and resource usage in
FPGA-based systems. The key idea of LEAPER is to transfer an ML-based
performance and resource usage model trained for a low-end edge environment to
a new, high-end cloud environment to provide fast and accurate predictions for
accelerator implementation. Experimental results show that LEAPER (1) provides,
on average across six workloads and five FPGAs, 85% accuracy when we use our
transferred model for prediction in a cloud environment with 5-shot learning
and (2) reduces design-space exploration time for accelerator implementation on
an FPGA by 10x, from days to only a few hours