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Graph Neural Networks for High-Level Synthesis Design Space Exploration by Lorenzo Ferretti; Andrea Cini; Georgios Zacharopoulos; Cesare Alippi; Laura Pozzi is a scholarly article available to read on EtoBox.

What is Graph Neural Networks for High-Level Synthesis Design Space Exploration about?

High-level Synthesis (HLS) Design-Space Exploration (DSE) aims at identifying Pareto-optimal synthesis configurations whose exhaustive search is unfeasible due to the design-space dimensionality and the prohibitive computational cost of the synthesis process. Within this framework, we address the design automation problem by proposing graph neural networks that jointly predict acceleration performance and hardware costs of a synthesized behavioral specification given optimization directives. Learned models can be used to rapidly approach the Pareto curve by guiding the DSE, taking into account performance and cost estimates. The proposed method outperforms traditional HLS-driven DSE approaches, by accounting for the arbitrary length of computer programs and the invariant properties of the input. We propose a novel hybrid control and dataflow graph representation that enables training the graph neural network on specifications of different hardware accelerators. Our approach achieves prediction accuracy comparable with that of state-of-the-art simulators without having access to analytical models of the HLS compiler. Finally, the learned representation can be exploited for DSE in un

Author

Lorenzo Ferretti; Andrea Cini; Georgios Zacharopoulos; Cesare Alippi; Laura Pozzi

Publisher

ACM

Published

2022

Language

EN