Algorithmic Techniques for Power Management and Optimization in Low-Power VLSI Design for Energy-Efficient Systems

To address these concerns, this paper proposes a novel low power management and optimization scheme for low power VLSI design based on both dynamic and static power sources. The proposed approach of design optimization integrates heuristic optimization algorithms namely; simulated annealing and genetic algorithms with machine learning models which optimizes the fine-tune of design parameters for a reduced power consumption. With the benchmark VLSI designs including ISCAS85, ISCAS89, and ITC99, the improved method indicated the power reduced by 25%-300/0 for different circuits. The method employed in this framework for controlling power this framework uses clock gating, power gating and the dynamic voltage and frequency scaling (DVFS). The simulation results utilizing the Cadence Virtuoso and Synopsys Design Compiler reveal the enhancement on the energy consumption to make the proposed approach appropriate for energy-sensitive applications such as Io'T, smart cell phones, as well as massive computing clusters. This research is significant in meeting to need for efficient systems at a time when power density and costs of operation are paramount.