ItemConference Paper

Enhancing Low-Power VLSI Design through AI-Based Simulation and Optimization

Title
Enhancing Low-Power VLSI Design through AI-Based Simulation and Optimization
Creator
Hundekari, Sheela; Onumajuru, Onyekachi Kelvin; Multani, Danish; Oluwakemi, Olutoye Timothy; Upreti, Kamal
Description
AI and ML techniques have dramatically influenced rapid developments in low-power VLSI design with fast advancements in device simulations and power optimization strategies. AI-based simulation tools are now used for accurate modeling of power consumption, improving thermal analysis, and quickening design iterations through the detection of inefficiencies and optimization of energy consumption. In fact, this work focuses on some AI-enabled methods of power reduction techniques such as voltage scaling, clock gating, and leakage current minimization with respect to a sustainable VLSI design. Moreover, a synthetic dataset is created to mimic the actual power consumption trend in VLSI circuits so that predictive modeling and regression techniques can be used for power estimation. Different regression models are used to check the predictive accuracy, and it was found that the highest R2 score was 0.85 by Linear Regression, while the worst was achieved by Decision Tree Regression at 0.50. Results of the correlation analysis and models by machine learning clearly indicate that the frequency and operating voltage are the major contributors to consumption power, while gate counts have a relatively insignificant contribution. Introduction of AI in VLSI simulation enables the enhancement of power efficiency while maintaining sustainability outcomes by optimizing energy usage and cost reduction in terms of computation. 2025 IEEE.
Source
Proceedings - 4th International Conference on Smart Technologies, Communication and Robotics 2025, STCR 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Design-optimization; Low-Powered VLSI; Machine Learning Models; Power Consumption; Sustainability; Synthetic Data
Coverage
Hundekari S., Pimpri Chinchwad University, Dept. of Computer Science, Pune, India; Onumajuru O.K., University of Hertfordshire, Dept. of Computer Science, United Kingdom; Multani D., Ust Global Solutions, Haryana, Gurgaon, India; Oluwakemi O.T., Olabisi Onabanjo University, Electrical and Electronics Engineering, Ago-Iwoye, Nigeria; Upreti K., Christ University, Dept. of Computer Science, Delhi NCR, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-835035753-0;
Format
online
Language
English
Type
Conference paper

Citation

Hundekari, Sheela; Onumajuru, Onyekachi Kelvin; Multani, Danish; Oluwakemi, Olutoye Timothy; Upreti, Kamal, “Enhancing Low-Power VLSI Design through AI-Based Simulation and Optimization,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/26218.

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