Latency Reduction in mmWave VLSI Circuits through Gravitational Learning

This research focuses on addressing latency issues in
millimeter-wave (mmWave) Very Large-Scale Integration (VLSI)
circuits by introducing a novel approach called Gravitational
Learning. The mmWave frequency range offers high data rates
but poses challenges due to increased latency. Conventional
techniques have limitations in reducing latency while maintaining
circuit performance. In this study, we propose a gravitational learning-based method that optimizes the circuit layout and parameters to minimize latency. By simulating the gravitational interactions between circuit components, the proposed approach effectively explores the design space and identifies configurations that lead to reduced latency. Our experimental results demonstrate significant latency reduction compared to existing techniques, highlighting the potential of gravitational learning in enhancing the performance of mmWave VLSI circuits.