5 citations · 8 across the 7 of their papers we have counts for
8 papers
GeNAS: Neural Architecture Search with Better Generalization
Joonhyun Jeong, Joonsang Yu, Geondo Park +2
Neural Architecture Search (NAS) aims to automatically excavate the optimal network architecture with superior test performance. Recent neural architecture search (NAS) approaches…
Pipe-BD: Pipelined Parallel Blockwise Distillation
Hongsun Jang, Jaewon Jung, Jaeyong Song +3
Training large deep neural network models is highly challenging due to their tremendous computational and memory requirements. Blockwise distillation provides one promising method…
Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration
Deokki Hong, Kanghyun Choi, Hye Yoon Lee +4
Co-exploration of an optimal neural architecture and its hardware accelerator is an approach of rising interest which addresses the computational cost problem, especially in low-pr…
EResFD: Rediscovery of the Effectiveness of Standard Convolution for Lightweight Face Detection
Joonhyun Jeong, Beomyoung Kim, Joonsang Yu +1
This paper analyzes the design choices of face detection architecture that improve efficiency of computation cost and accuracy. Specifically, we re-examine the effectiveness of the…
It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher
Kanghyun Choi, Hye Yoon Lee, Deokki Hong +4
Model quantization is considered as a promising method to greatly reduce the resource requirements of deep neural networks. To deal with the performance drop induced by quantizatio…
NN-LUT: Neural Approximation of Non-Linear Operations for Efficient Transformer Inference
Joonsang Yu, Junki Park, Seongmin Park +4
Non-linear operations such as GELU, Layer normalization, and Softmax are essential yet costly building blocks of Transformer models. Several prior works simplified these operations…
DANCE: Differentiable Accelerator/Network Co-Exploration
Kanghyun Choi, Deokki Hong, Hojae Yoon +3
To cope with the ever-increasing computational demand of the DNN execution, recent neural architecture search (NAS) algorithms consider hardware cost metrics into account, such as…
Network Recasting: A Universal Method for Network Architecture Transformation
Joonsang Yu, Sungbum Kang, Kiyoung Choi
This paper proposes network recasting as a general method for network architecture transformation. The primary goal of this method is to accelerate the inference process through th…