Dynamic Reservoir Computing Architecture for Enhanced Phonon-mediated Exciton Transport in Perovskite-Based AI Hardware

**Abstract:** This paper introduces a novel hardware architecture leveraging dynamic reservoir computing principles integrated with specifically engineered perovskite materials to enhance phonon-mediated exciton transport for AI acceleration. Existing perovskite-based devices suffer from exciton bottlenecking and energy loss, limiting their computational efficiency. We propose a system that dynamically modulates the perovskite's vibrational modes (phonons) using integrated micro-actuators, creating a dynamically adjustable reservoir capable of efficiently channeling excitons for rapid computation. Mathematical modeling of exciton dynamics within the modulated perovskite is presented, coupled with a detailed architectural design and a proof-of-concept experimental roadmap. This integrated approach yields a substantial advantage in computation speed and efficiency for AI applications compared to static perovskite devices, offering a pathway toward low-power, high-performance AI hardware.