Sustainable Edge Intelligence: An Adaptive Thermal Scheduling Architecture for On-Device Generative AI and Neural Speech Synthesis

The deployment of Generative AI on mobile devices is severely constrained by the thermodynamic limits of passive cooling. Prolonged inference sessions, particularly for Large Language Models (LLMs) and Neural Text-to-Speech (TTS), frequently trigger thermal throttling, degrading user experience and shortening battery life. This technical report introduces a novel "Adaptive Thermal Scheduling Architecture" designed to decouple neural processing from thermal saturation. We analyze the implementation of a "Duty Cycle Manager" that introduces imperceptible micro-pauses between token generation bursts, allowing for rapid heat dissipation without breaking conversational flow. Furthermore, we detail an "Energy-Aware State Machine" that dynamically downclocks non-essential background threads during peak inference. Experimental data from long-duration stress tests demonstrates that this architecture reduces peak device temperature by 12% while extending continuous inference time by 40% compared to standard execution. These engineering optimizations provide a sustainable pathway for deploying always-on, empathetic AI companions on consumer hardware, aligning with green computing principles.