**Energy-Efficient Edge AI: Reinforcement Learning for Adapt

Energy-Efficient Edge AI: Reinforcement Learning for Adaptive Power Management

In edge AI systems, energy efficiency is a pressing concern due to the proliferation of IoT devices and the associated power consumption. One promising approach to mitigate this issue is to design a reinforcement learning algorithm that incentivizes edge AI devices to proactively 'sleep' and reduce energy consumption when their predictive model's confidence in accuracy drops below a certain threshold.

Problem Formulation

We can model this problem as a Markov Decision Process (MDP), where the state space consists of the device's energy level, the model's confidence level, and the current task. The action space includes 'sleep' or 'keep running'. The reward function can be defined as a combination of energy efficiency and accuracy, with a higher reward for sleeping when the model's confidence is low.

Algorithm Design

1. State Representation: Define a vector representation of the state, ...

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