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Found 4 papers, 1 papers with code
Memory-efficient Energy-adaptive Inference of Pre-Trained Models on Batteryless Embedded Systems
Recent works on compression mostly focus on time and memory, but often ignore energy dynamics or significantly reduce the accuracy of pre-trained DNNs.
RecNet: Early Attention Guided Feature Recovery
Uncertainty in sensors results in corrupted input streams and hinders the performance of Deep Neural Networks (DNN), which focus on deducing information from data.
Zygarde: Time-Sensitive On-Device Deep Inference and Adaptation on Intermittently-Powered Systems
We propose Zygarde -- which is an energy -- and accuracy-aware soft real-time task scheduling framework for batteryless systems that flexibly execute deep learning tasks1 that are suitable for running on microcontrollers.
Intermittent Learning: On-Device Machine Learning on Intermittently Powered System
This paper introduces intermittent learning - the goal of which is to enable energy harvested computing platforms capable of executing certain classes of machine learning tasks effectively and efficiently.
