Power, Performance, and Area (PPA) Optimization Framework for Energy-Efficient VLSI System Design Using Reinforcement Learning

Abstract

The recent development of the VLSI systems that can be utilised in edge computing, artificial intelligence, and high-performance embedded systems has considerably compelled the power limitation, thus rendering energy efficiency an important design goal. There is a general need to have a perfect balance of power, performance and area (PPA) which is a challenging aspect to accomplish because it is a multidimensional domain of design. Traditional optimization methods, such as heuristic and rule-based methods, are not always effective in dealing with multi-objective trade off and are not flexible in different design constraints. To address such constraints, this paper suggests a reinforcement learning (RL)-based PPA optimization bench that belongs to navigation of the entire design and finding the best design parameters via incessant engagement with the VLSI design environment. The prodromal framework reformulates the optimization problem as a sequence of making decision in order to allow the dynamic optimization of major design variables, including supply voltage, operating frequency and architectural designs. To strike a balance between conflicting objectives, a reward-based learning mechanism is adopted to achieve minimised power use and area and maximised performance simultaneously. According to experimental findings, the suggested RL-based solution can generate an impressive decrease in the total power consumption, as well as significant enhancement of the energy efficiency in terms of Energy-Delay Product (EDP) and Power-Delay Product (PDP), in comparison to traditional optimization tools. The framework is also more convergent with the optimal solutions and relies less on handicraft. All in all, the suggested methodology offers a scalable, adaptive and smart solution to PPA optimization, which can be strongly applicable in future next generation energy efficient VLSI system design under semiconductor of complex environment.