22 citations · 42 across the 2 of their papers we have counts for
3 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…
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…