131 citations · 131 across the 2 of their papers we have counts for
2 papers
cs.LG2021
MOHAQ: Multi-Objective Hardware-Aware Quantization of Recurrent Neural Networks
Nesma M. Rezk, Tomas Nordström, Dimitrios Stathis +3
The compression of deep learning models is of fundamental importance in deploying such models to edge devices. The selection of compression parameters can be automated to meet chan…
cs.NE2019★ 131 cited
Recurrent Neural Networks: An Embedded Computing Perspective
Nesma M. Rezk, Madhura Purnaprajna, Tomas Nordström +1
Recurrent Neural Networks (RNNs) are a class of machine learning algorithms used for applications with time-series and sequential data. Recently, there has been a strong interest i…