153 citations · 197 across the 4 of their papers we have counts for
6 papers
QFT: Post-training quantization via fast joint finetuning of all degrees of freedom
Alex Finkelstein, Ella Fuchs, Idan Tal +3
The post-training quantization (PTQ) challenge of bringing quantized neural net accuracy close to original has drawn much attention driven by industry demand. Many of the methods e…
Tiled Squeeze-and-Excite: Channel Attention With Local Spatial Context
Niv Vosco, Alon Shenkler, Mark Grobman
In this paper we investigate the amount of spatial context required for channel attention. To this end we study the popular squeeze-and-excite (SE) block which is a simple and ligh…
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…
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…
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…
Prefect Klein tunneling in anisotropic graphene-like photonic lattices
Omri Bahat-Treidel, Or Peleg, Mark Grobman +3
We study the scattering of waves off a potential step in deformed honeycomb lattices. For small deformations below a critical value, perfect Klein tunneling is obtained. This means…