2 citations · 2 across the 1 of their papers we have counts for
3 papers
cs.LG2020★ 2 cited
Weight Equalizing Shift Scaler-Coupled Post-training Quantization
Jihun Oh, SangJeong Lee, Meejeong Park +2
Post-training, layer-wise quantization is preferable because it is free from retraining and is hardware-friendly. Nevertheless, accuracy degradation has occurred when a neural netw…
cs.DC2018
Co-Design of Deep Neural Nets and Neural Net Accelerators for Embedded Vision Applications
Kiseok Kwon, Alon Amid, Amir Gholami +3
Deep Learning is arguably the most rapidly evolving research area in recent years. As a result it is not surprising that the design of state-of-the-art deep neural net models proce…
cs.NE2018
SqueezeNext: Hardware-Aware Neural Network Design
Amir Gholami, Kiseok Kwon, Bichen Wu +5
One of the main barriers for deploying neural networks on embedded systems has been large memory and power consumption of existing neural networks. In this work, we introduce Squee…