  A typical optimization of customized accelerators for error-tolerant
applications such as multimedia, recognition, and classification is to replace
traditional arithmetic units like multipliers and adders with the approximate
ones to enhance energy efficiency while adhering to accuracy requirements.
However, the plethora of arithmetic units and diverse approximate unit options
result in an exceedingly large design space. Therefore, there is a pressing
need for an end-to-end design framework capable of navigating this intricate
design space for approximation optimization. Traditional methods relying on
simulation-based or blackbox model evaluations suffer from either high
computational costs or limitations in accuracy and scalability, posing
significant challenges to the optimization process. In this paper, we propose a
Graph Neural Network (GNN) model that leverages the physical connections of
arithmetic units to capture their influence on the performance, power, area
(PPA), and accuracy of the accelerator. Particularly, we notice that critical
path plays a key role in node feature of the GNN model and having it embedded
in the feature vector greatly enhances the prediction quality of the models. On
top of the models that allow rapid and efficient PPA and accuracy prediction of
various approximate accelerator configurations, we can further explore the
large design space effectively and build an end-to-end accelerator
approximation framework named ApproxPilot to optimize the accelerator
approximation. Our experimental results demonstrate that ApproxPilot
outperforms state-of-the-art approximation optimization frameworks in both
performance and hardware overhead with the same accuracy constraints.
