Proceedings of the 2021 Drone Systems Engineering and Rapid Simulation and Performance Evaluation: Methods and Tools Proceeding 2021
DOI: 10.1145/3444950.3447283
|View full text |Cite
|
Sign up to set email alerts
|

GANNoC: A Framework for Automatic Generation of NoC Topologies using Generative Adversarial Networks

Abstract: We propose GANNoC, a framework for automatic generation of customized Network-on-Chip (NoC) topologies, which exploits generative adversarial networks (GANs) learning capabilities. We define the problem of NoC generation as a graph generation problem, and train a GAN to produce such graphs. We further present a Reward-WGAN (RWGAN) architecture, based on the Wasserstein GAN (WGAN). It is coupled to a reward network enabling to steer the resulting generative system towards topologies having desired properties. W… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
3
0

Year Published

2022
2022
2022
2022

Publication Types

Select...
1

Relationship

1
0

Authors

Journals

citations
Cited by 1 publication
(3 citation statements)
references
References 11 publications
0
3
0
Order By: Relevance
“…In [7], the GANNoC framework exploits Convolutional Neural Network (CNN) and GANs to generate irregular NoC topologies minimizing the number of inter-router connections. In this work, we leverage GANs to reduce the design space exploration for heterogeneous NoC composed of a diversity of routers.…”
Section: Related Workmentioning
confidence: 99%
See 2 more Smart Citations
“…In [7], the GANNoC framework exploits Convolutional Neural Network (CNN) and GANs to generate irregular NoC topologies minimizing the number of inter-router connections. In this work, we leverage GANs to reduce the design space exploration for heterogeneous NoC composed of a diversity of routers.…”
Section: Related Workmentioning
confidence: 99%
“…Lately, the concept of "Reward WGAN" (RWGAN) was introduced [7], [11]. It adds a third network, called Reward, to provide further guidance to the Generator's learning.…”
Section: Design Space Pruning Toolmentioning
confidence: 99%
See 1 more Smart Citation