Integrating Traditional Low-Power Techniques with AI/ML for Low-Power and High-Efficiency VLSI Chips

Continuous miniaturization in VLSI fabrication has enhanced integration density and computational capability but has also intensified challenges related to energy efficiency and thermal reliability. Conventional design strategies such as voltage scaling, clock gating, power gating, and multi-threshold CMOS remain vital for managing dynamic and leakage power. However, growing circuit complexity and heterogeneous workloads limit the effectiveness of traditional methods alone. Consequently, leading semiconductor industries are investing extensively in low-power design research, highlighting its industrial importance. This paper presents an integrated approach that combines traditional low-power strategies with Artificial Intelligence (AI) and Machine Learning (ML) to achieve higher optimization efficiency in VLSI chips. ML-based predictive models estimate power-performance trade-offs and optimize resource allocation, while reinforcement learning dynamically controls parameters such as voltage and frequency. The comparative evaluation demonstrates that this hybrid integration improves scalability, flexibility, and efficiency—signaling a promising direction for next-generation VLSI design.