22 citations · 44 across the 3 of their papers we have counts for
3 papers
cs.LG2020★ 2 cited
Exploring Neural Networks Quantization via Layer-Wise Quantization Analysis
Shachar Gluska, Mark Grobman
Quantization is an essential step in the efficient deployment of deep learning models and as such is an increasingly popular research topic. An important practical aspect that is n…
cs.LG2019★ 20 cited
Fighting Quantization Bias With Bias
Alexander Finkelstein, Uri Almog, Mark Grobman
Low-precision representation of deep neural networks (DNNs) is critical for efficient deployment of deep learning application on embedded platforms, however, converting the network…
cs.LG2019★ 22 cited
Same, Same But Different - Recovering Neural Network Quantization Error Through Weight Factorization
Eldad Meller, Alexander Finkelstein, Uri Almog +1
Quantization of neural networks has become common practice, driven by the need for efficient implementations of deep neural networks on embedded devices. In this paper, we exploit…