AI-Assisted Ultra-Low Power IoT Architecture with Predictive DVFS and Real-Time Energy Monitoring

Abstract

The increasing deployment of wearable and Internet of Things (IoT) devices has created a significant demand for intelligent ultra-low-power hardware architectures capable of operating under strict energy constraints. This work presents an AI-assisted Ultra-Low Power IoT architecture that combines predictive workload analysis, Dynamic Voltage and Frequency Scaling (DVFS), clock gating, and real-time energy monitoring to improve energy efficiency while maintaining system performance. Unlike conventional threshold-based power management techniques, the proposed architecture incorporates a lightweight AIinspired workload prediction module that analyzes historical workload patterns and estimates future computational demand. Based on the predicted workload, the Power Management Unit (PMU) dynamically selects appropriate operating modes and frequency levels to reduce unnecessary power consumption. An integrated Energy Monitoring Unit (EMU) continuously tracks energy utilization across different operating states, enabling energy-aware system optimization. The complete architecture is described using Verilog HDL and is suitable for FPGA implementation and validation. Experimental evaluation demonstrates adaptive power management, efficient workload-driven frequency scaling, and improved energy utilization compared to static operating schemes. The proposed design offers a scalable and hardwareefficient solution for next-generation wearable, sensor, and edge-computing applications requiring intelligent energy management and extended battery life.