Hierarchical Source-to-Post-Route QoR Prediction in High-Level Synthesis
  with GNNs
Mingzhe Gao Jieru Zhao Zhe Lin Minyi Guo
High-level synthesis (HLS) notably speeds up the hardware design process by
avoiding RTL programming. However, the turnaround time of HLS increases
significantly when post-route quality of results (QoR) are considered during
optimization. To tackle this issue, we propose a hierarchical post-route QoR
prediction approach for FPGA HLS, which features: (1) a modeling flow that
directly estimates latency and post-route resource usage from C/C++ programs;
(2) a graph construction method that effectively represents the control and
data flow graph of source code and effects of HLS pragmas; and (3) a
hierarchical GNN training and prediction method capable of capturing the impact
of loop hierarchies. Experimental results show that our method presents a
prediction error of less than 10% for different types of QoR metrics, which
gains tremendous improvement compared with the state-of-the-art GNN methods. By
adopting our proposed methodology, the runtime for design space exploration in
HLS is shortened to tens of minutes and the achieved ADRS is reduced to 6.91%
on average.