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arXiv · 2407.03792

NeuroSteiner: A Graph Transformer for Wirelength Estimation

Sahil Manchanda·Dana Kianfar·Markus Peschl·Romain Lepert·Michaël Defferrard
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Abstract

A core objective of physical design is to minimize wirelength (WL) when placing chip components on a canvas. Computing the minimal WL of a placement requires finding rectilinear Steiner minimum trees (RSMTs), an NP-hard problem. We propose NeuroSteiner, a neural model that distills GeoSteiner, an optimal RSMT solver, to navigate the cost--accuracy frontier of WL estimation. NeuroSteiner is trained on synthesized nets labeled by GeoSteiner, alleviating the need to train on real chip designs. Moreover, NeuroSteiner's differentiability allows to place by minimizing WL through gradient descent. On ISPD 2005 and 2019, NeuroSteiner can obtain 0.3% WL error while being 60% faster than GeoSteiner, or 0.2% and 30%.

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Sahil Manchanda, Dana Kianfar, Markus Peschl, Romain Lepert, Michaël Defferrard. 2024-07-04. NeuroSteiner: A Graph Transformer for Wirelength Estimation. https://arxiv.org/abs/2407.03792

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