Exposing Reliability Degradation and Mitigation in Approximate DNNs Under Permanent Faults
Author:
Affiliation:
1. Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Hardware and Architecture,Software
Link
http://xplorestaging.ieee.org/ielx7/92/10079225/10028723.pdf?arnumber=10028723
Reference52 articles.
1. DyVEDeep: Dynamic variable effort deep neural networks;ganapathy;arXiv 1704 01137,2017
2. CAxCNN: Towards the Use of Canonic Sign Digit Based Approximation for Hardware-Friendly Convolutional Neural Networks
3. Exploring Fault-Energy Trade-offs in Approximate DNN Hardware Accelerators
4. Approximate Random Dropout for DNN training acceleration in GPGPU
5. The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]
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