NExUME: Adaptive Training and Inference for DNNs under Intermittent Power Environments
Cyan Subhra Mishra Deeksha Chaudhary Jack Sampson Mahmut Kandemir Chita R. Das
The deployment of Deep Neural Networks (DNNs) in energy-constrained environments, such as Energy Harvesting Wireless Sensor Networks (EH-WSNs), introduces significant challenges due to the intermittent nature of power availability. This study introduces NExUME, a novel training methodology designed specifically for DNNs operating under such constraints. We propose a dynamic adjustment of training parameters—dropout rates and quantization levels—that adapt in real-time to the available energy, which varies in energy harvesting scenarios.

This approach utilizes a model that integrates the characteristics of the network architecture and the specific energy harvesting profile. It dynamically adjusts training strategies, such as the intensity and timing of dropout and quantization, based on predictions of energy availability. This method not only conserves energy but also enhances the network’s adaptability, ensuring robust learning and inference capabilities even under stringent power constraints. Our results show a 6% to 22% improvement in accuracy over current methods, with an increase of less than 5% in computational overhead. This paper details the development of the adaptive training framework, describes the integration of energy profiles with dropout and quantization adjustments, and presents a comprehensive evaluation using real-world data. Additionally, we introduce a novel dataset aimed at furthering the application of energy harvesting in computational settings.
Intermittent Computing, Energy Harvesting, Intermittency Aware Training, Hardware-Software codesign
We introduce NExUME, a framework for training DNNs in settings with intermittent power, like remote sensors. It dynamically adjusts training to fluctuating energy, making networks adaptable and reliable even in energy-constrained environments.