Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives

1 code implementation • 12 Feb 2021 • Takuya Narihira, Javier Alonsogarcia, Fabien Cardinaux, Akio Hayakawa, Masato Ishii, Kazunori Iwaki, Thomas Kemp, Yoshiyuki Kobayashi, Lukas Mauch, Akira Nakamura, Yukio Obuchi, Andrew Shin, Kenji Suzuki, Stephen Tiedmann, Stefan Uhlich, Takuya Yashima, Kazuki Yoshiyama

While there exist a plethora of deep learning tools and frameworks, the fast-growing complexity of the field brings new demands and challenges, such as more flexible network design, speedy computation on distributed setting, and compatibility between different tools.

Iteratively Training Look-Up Tables for Network Quantization

no code implementations • NIPS Workshop CDNNRIA 2018 • Fabien Cardinaux, Stefan Uhlich, Kazuki Yoshiyama, Javier Alonso Garcia, Lukas Mauch, Stephen Tiedemann, Thomas Kemp, Akira Nakamura

For each layer, we learn a value dictionary and an assignment matrix to represent the network weights.

Network Pruning Quantization

Mixed Precision DNNs: All you need is a good parametrization

2 code implementations • ICLR 2020 • Stefan Uhlich, Lukas Mauch, Fabien Cardinaux, Kazuki Yoshiyama, Javier Alonso Garcia, Stephen Tiedemann, Thomas Kemp, Akira Nakamura

Since choosing the optimal bitwidths is not straight forward, training methods, which can learn them, are desirable.

Quantization

Iteratively Training Look-Up Tables for Network Quantization

no code implementations • 13 Nov 2018 • Fabien Cardinaux, Stefan Uhlich, Kazuki Yoshiyama, Javier Alonso García, Stephen Tiedemann, Thomas Kemp, Akira Nakamura

In this paper we introduce a training method, called look-up table quantization, LUT-Q, which learns a dictionary and assigns each weight to one of the dictionary's values.

object-detection Object Detection +1