Search Results for author:
Found 3 papers, 2 papers with code
Designing Extremely Memory-Efficient CNNs for On-device Vision Tasks
In this paper, we introduce a memory-efficient CNN (convolutional neural network), which enables resource-constrained low-end embedded and IoT devices to perform on-device vision tasks, such as image classification and object detection, using extremely low memory, i. e., only 63 KB on ImageNet classification.
CAFO: Feature-Centric Explanation on Time Series Classification
This improvement in feature-wise ranking enhances our understanding of feature explainability in MTS.
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
