PPA-Optimized VLSI Architecture for Energy-Efficient Digital Systems

The growing need in small and high-density electronic devices has increased the significance of the design of power-constrained and performance-efficient Very Large Scale Integration (VLSI) architectures. The conventional methodologies used in the design of VLSI typically focus on performance improvement, which further induces power consumption and thermal reliability degradation thus negatively affecting the scaling of the current energy-starved systems. This study introduces a new VLSI Power-Performance-Area (PPA) optimised architecture which strategically combines multi-level power saving methods and adaptive performance scaling and area-aware design with regard to these issues. The suggested architecture integrates volatile voltage and frequency adjustment (DVFS), micro-gated power gating, and near-threshold functioning so as to allow smart power management of a wide range of workloads. A common design architecture is used, in which power and performance trade-offs are dynamically tuned with the balance at the hardware level and area efficiency optimized with the help of logic sharing and small memory designs. Industry-standard simulation and synthesis of the architecture is carried out on a 45nm CMOS process, and shows significant improvements in the energy efficiency and PPA measurements. The proposed structure can reduce total power by up to 38 watts, performanceperwatts by 42 percent, and retain area overhead, as a result of architectural reuse, within 5 per cent of a traditional baseline architecture. Besides, the architecture will be operationally integrity in the near threshold voltages, therefore the architecture will run low on energy without any critical level performance loss. The PPA-optimised-based approach in this work is scalable and is applicable to a large variety of embedded and edge computers whose energy sustainability, small silicon protocols, and reliable performance is paramount. The research paper is a holistic approach and previously tested findings that present a viable solution to existing VLSI design bottlenecks as part of the trend towards green and sustainable computing. The results provide a good basis upon which new studies in AI-based dynamic power management and cross-layer optimization of energy-conscious VLSI systems can be conducted.