Implementing Deep Learning for Battery-Operated Smart Devices: The Power Consumption using Optimization Techniques

Deploying deep learning models on batterypowered smart devices is challenging due to the dual requirements to have both high computational performance and low energy consumption. With supporting voice recognition, image processing, and environment sensing and so forth has raised the bar for resource efficiency and execution latency, the integration of deep learning on these devices requiring energy efficient execution for battery life and user satisfaction. This work fits at the intersection of deep learning and energy optimization question infrastructure, and its purpose is to understand how to exploit advanced optimization mechanisms to reduce energy use through the deployment of deep learning model on the edge without sacrificing the model accuracy and responsiveness. The research also analyzes different techniques for optimization of the model through model quantization, pruning, knowledge distillation, and running the model on low-parameter architectures such as MobileNets and SqueezeNet. The applicability of these methods is tested in practical environment with popular battery-operated devices like Raspberry Pi, Arduino, and smartphones. Power profiling and profiling performance are performed over various deep learning applications (image classification, NLP and object detection) to determine tradeoffs among energy consumption, latency and model accuracy. Experimental data show that a simultaneous use of several optimization strategies can reduce the power consumption massively ($50-60 \%$) yet the performance is still acceptable. Additionally, dynamic adaptation schemes, such as runtime model switching according to the battery level, or current task complexity, are shown to be the key for efficient power management. The results of this work highlight the necessity of co-designing hardware-aware deep learning models and energy-aware deployment strategies. This work presents a novel methodology for power optimization in deep learning on smart devices, and, it is anticipated that it will help enable sustainable intelligent, and autonomous edge computing systems. It is a guide for further progress towards energyefficient AI, which is essential to the next generation of ubiquitous, battery-powered intelligent devices.