ApproxGNN: A Pretrained GNN for Parameter Prediction in Design Space Exploration for Approximate Computing

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0197688" target="_blank" >RIV/00216305:26230/26:0197688 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1109/ICCAD66269.2025.11240776" target="_blank" >http://dx.doi.org/10.1109/ICCAD66269.2025.11240776</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICCAD66269.2025.11240776" target="_blank" >10.1109/ICCAD66269.2025.11240776</a>

Result language

angličtina

Original language name

ApproxGNN: A Pretrained GNN for Parameter Prediction in Design Space Exploration for Approximate Computing

Original language description

Approximate computing offers promising energy efficiency benefits for error-tolerant applications, but discovering optimal approximations requires extensive design space exploration (DSE). Predicting the accuracy of circuits composed of approximate components without performing complete synthesis remains a challenging problem. Current machine learning approaches used to automate this task require retraining for each new circuit configuration, making them computationally expensive and time-consuming. This paper presents ApproxGNN, a construction methodology for a pre-trained graph neural network model predicting QoR and HW cost of approximate accelerators employing approximate adders from a library. This approach is applicable in DSE for assignment of approximate components to operations in accelerator. Our approach introduces novel component feature extraction based on learned embeddings rather than traditional error metrics, enabling improved transferability to unseen circuits. ApproxGNN models can be trained with a small number of approximate components, supports transfer to multiple prediction tasks, utilizes precomputed embeddings for efficiency, and significantly improves accuracy of the prediction of approximation error. On a set of image convolutional filters, our experimental results demonstrate that the proposed embeddings improve prediction accuracy (mean square error) by 50% compared to conventional methods. Furthermore, the overall prediction accuracy is 30% better than statistical machine learning approaches without fine-tuning and 54% better with fast finetuning.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA24-10990S" target="_blank" >GA24-10990S: Hardware-Aware Machine Learning: From Automated Design to Innovative and Explainable Solutions</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2025 IEEE/ACM International Conference On Computer Aided Design (ICCAD)

ISBN

979-8-3315-1560-7

ISSN

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e-ISSN

—

Number of pages

8

Pages from-to

1-8

Publisher name

IEEE

Place of publication

Munich, Germany

Event location

Munich

Event date

Oct 26, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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