380 citations · 923 across the 38 of their papers we have counts for
5 papers · 1 filter
Precision Highway for Ultra Low-Precision Quantization
Eunhyeok Park, Dongyoung Kim, Sungjoo Yoo +1
Neural network quantization has an inherent problem called accumulated quantization error, which is the key obstacle towards ultra-low precision, e.g., 2- or 3-bit precision. To re…
ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation
Xiaoliang Dai, Peizhao Zhang, Bichen Wu +10
This paper proposes an efficient neural network (NN) architecture design methodology called Chameleon that honors given resource constraints. Instead of developing new building blo…
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
Bichen Wu, Xiaoliang Dai, Peizhao Zhang +7
Designing accurate and efficient ConvNets for mobile devices is challenging because the design space is combinatorially large. Due to this, previous neural architecture search (NAS…
Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search
Bichen Wu, Yanghan Wang, Peizhao Zhang +3
Recent work in network quantization has substantially reduced the time and space complexity of neural network inference, enabling their deployment on embedded and mobile devices wi…
Value-aware Quantization for Training and Inference of Neural Networks
Eunhyeok Park, Sungjoo Yoo, Peter Vajda
We propose a novel value-aware quantization which applies aggressively reduced precision to the majority of data while separately handling a small amount of large data in high prec…