Enabling Inference and Training of Deep Learning Models for AI Applications on IoT Edge Devices

Author:

Sharma Divyasheel,Sarkar Santonu

Publisher

Springer International Publishing

Reference51 articles.

1. General Data Protection Regulation (GDPR) (https://gdpr-info.eu/)

2. Edge AI Software Market by Component (Solutions and Services), Data Source, Application (Autonomous Vehicles, Access Management, Video Surveillance, Remote Monitoring & Predictive Maintenance, Telemetry), Vertical, and Region – Global Forecast to 2023. Markets and Markets (https://www.researchandmarkets.com/reports/4752886/edge-ai-software-market-by-component-solutions)

3. Deep learning models with pre-trained weights. (https://keras.io/api/applications/)

4. Cheng, Y., Wang, D., Zhou, P., & Zhang, T. (2018). Model compression and acceleration for deep neural networks: The principles, progress, and challenges. IEEE Signal Processing Magazine, 35(1), 126–136.

5. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770–778).

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