AI/ML-Driven Electronic Design Automation Framework for Quantum-Aware VLSI Circuit Synthesis and Optimization in High-Performance Computing Applications

Abstract

Increased development of high-performance computing (HPC) has increased the pressure on new paradigms in VLSI circuit design, as device scaling is approaching scaling limits where quantum effects become important. Conventional electronic design automation (EDA) processes have difficulties in dealing with nonlinear interactions that occur when quantum tunnelling, leakage currents, and probabilistic switching are also present in the deeply scaled technology. To overcome them, this paper suggests an all-encompassing AI/ML-based EDA architecture, which incorporates quantum-aware modelling, predictive synthesis, and adaptive optimization of future HPC-oriented next-generation VLSI systems. The framework has incorporated machine learning-based parametric estimation, reinforcement learning on layout exploration and physics-guided neural models: non-classical effects in nanoscale transistors. Furthermore, the system uses generative learning algorithms to create multi-objective design trade-offs in terms of timing, power, area and quantum reliability. The hybrid digital-quantum design flow is presented, allowing to easily interchange the classical EDA operations and quantum-inspired device tests. Nanometer-scale benchmark circuit confirmation the efficiency of synthesis, accuracy of leakage prediction and rate of convergence of optimization are shown to be much enhanced with respect to more traditional EDA pipelines. The given methodology puts the emphasis on the role of intelligent automation as the means of guiding the VLSI research towards the point of quantum-awareness as the key to ensuring reliability, scalability, and energy efficiency in an HPC system.