Machine Learning–Assisted VLSI Design Automation for Optimized Timing, Power, and Area in Advanced Semiconductor Systems

Abstract

Advanced semiconductor design space exploration has grown to be a highly complex, demanding and expensive functionality, especially in finding optimal trade-offs between conflicting goals like timing, power consumption and silicon area. The conventional design automation methods can be either heuristic or rule based and both methods find it difficult to equivalently explore the design space that grows exponentially. To overcome these drawbacks, this paper suggests a reinforcement learning (RL)-based design automation framework to intelligent and adaptive optimization of the VLSI systems. The proposed framework represents the approach to the design procedure of VLSI as a sequence of decisions, an RL agent is required to operate in the design environment and coordinate the parameters of the design, voltage, frequency, and architectural configurations, with the help of an iterative loop. Reward-driven feedback enables the agent to determine an optimal policy and targets to locate a trade-off between timing, power, and area. The RL framework provides a way of searching through complex design spaces efficiently without human intervention by making increased versions of decisions by trial and error over time through the observed performance. The experimental data proves that the suggested RL-based methodology can achieve major breakthroughs in the key VLSI performance indicators, such as a decrease in power consumption, a minimum of critical path delay, and enhanced area occupancy relative to traditional baseline solution strategies. In addition, the learning-based optimization has stable convergence properties as well as flexibility to different design conditions. Generally, the results point to the reinforcement learning as an important technique in next-generation VLSI design automation to allow scaleable, efficient, and intelligent optimization of the power-performance-area (PPA) of advanced semiconductor architectures.