no code implementations • 24 Nov 2020 • Lukas Mauch, Stephen Tiedemann, Javier Alonso Garcia, Bac Nguyen Cong, Kazuki Yoshiyama, Fabien Cardinaux, Thomas Kemp
Usually, we compute the proxy for all DNNs in the network search space and pick those that maximize the proxy as candidates for optimization.
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.
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.
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.