A Secure Energy-Aware Hybrid ESP32–Raspberry Pi Architecture for On-Demand Edge AI (Version 1.1)

Background / Problem: Deploying complex Artificial Intelligence models on portable edge devices faces significant challengesregarding power constraints and computational latency. Traditional embedded systems often compromise between continuousoperational availability and battery longevity, limiting their effectiveness in secure, off-grid field applications. This trade-offnecessitates a new architectural approach that decouples low-power control from high-performance computing.Proposed System: This paper presents the design and implementation of the G-80 AI Pro (2026), a secure, multi-modal embeddedsystem built upon a heterogeneous dual-processor architecture. The proposed system delegates low-latency peripheralmanagement, user interface logic, and security protocols to an "always-on" ESP32 microcontroller, while utilizing a Raspberry Pi5 as an "on-demand" inference engine for resource-intensive computer vision and voice analysis tasks. A custom UART bridgefacilitates secure JSON-based command exchange, ensuring seamless synchronization between the low-power controller and thehigh-performance AI core.