Neuromile: A Continual Meta-Learning-Based AI Deployment Framework for Energy-Aware Personalized Inference at the Edge

Abstract

NeuroMile is an alternatives-based, new AI architecture that combines continual learning, meta-learning, and dynamic energy optimization to perform real-time and accurate inference on markets and end gadgets. Engineered to address the resource limitation of embedded and wearable systems, the task-aware memory encoder and the adaptive-modulation of inference depth and quantization level, NeuroMile has been developed to support a modularized architecture. Such adaptations have dynamic contextual feedback, such as battery level, activity and complexity of task.

Relative evaluations in three edge-related benchmarks, PAMAP2 (human activity recognition), EdgeSpeech (voice command recognition), and CIFAR-100 (few-shot image classification) show that NeuroMile can reach 88.9% at 1.1W power consumption as opposed to the full-precision baseline of 89.6% accuracy at 2.8W power consumption. This shows a decrease of 60 percent of energy consumed with less than 1 percent loss of accuracy. Besides, NeuroMile can train much faster in terms of task-specific fine-tuning, with only 7.8s required compared to conventional meta-learning baselines, including MAML (10.3s) and FedAvg (18.1s).

These findings put NeuroMile as a feasible and smart edge inference architecture that trades-off among accuracy, energy-efficiency, and flexibility. It is applicable to mobile robotics, wearable health-monitors, as well as real-time IoT installations. The future work will consist of a federated learning to enable edge adaptation to be secretive and reinforcement learning-based self-optimizing edge control policies to further enlarge the sustainability and personalization aspect in this Computational model.