Attention‐based model for dynamic IR drop prediction with multi‐view features-Reference-Cited by-同舟云学术

Attention‐based model for dynamic IR drop prediction with multi‐view features

Published:2023-07 Issue:13 Volume:59 Page:
ISSN:0013-5194
Container-title:Electronics Letters
language:en
Short-container-title:Electronics Letters

Author:

Zhu Wenhao¹²,Liu Wu¹^ORCID

Affiliation:

1. National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong University Shanghai China

2. Department of Micro/Nano Electronics School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

Abstract

AbstractDynamic IR drop prediction based on machine learning has been studied in recent years. However, most proposed models used all input features extracted from circuits or manually selected parts of raw features as inputs, which failed to differentiate the order of priority among input features in a flexible manner. In this paper, QuantumForest to vector‐based dynamic IR drop prediction is introduced. With the sparse attention mechanism brought by QuantumForest, important attributes of circuits are weighed more heavily than others. A new multi‐view feature creation method is also proposed and a novel regional distance feature is built up subsequently. The performance is evaluated on two chip designs with real simulation vectors. The experiment results indicate that the prediction result of the method outperforms other prominent methods for dealing with machine learning based IR drop analysis, reaching an average MAE of only 1.457 on two designs.

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering

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