Abstract

As Very Large Scale Integration (VLSI) technology advances, the need to efficiently balance cost and performance in VLSI systems becomes paramount. To address this challenge, we propose a novel approach that leverages the RMSPROP algorithm for assisted design space exploration. The RMSPROP algorithm, which has proven effective in the field of deep learning optimization, is adapted to navigate the complex design space of VLSI systems. By integrating RMSPROP into the design space exploration process, we can intelligently search for optimal trade-offs between cost and performance, leading to highly efficient VLSI designs. Our experimental results demonstrate the effectiveness of the RMSPROP algorithm-assisted design space exploration, showcasing significant improvements in cost-performance trade-offs compared to traditional design methodologies. This research opens new avenues for designing VLSI systems with improved efficiency, enabling the realization of high-performance yet cost-effective integrated circuits.