6 citations · 24 across the 8 of their papers we have counts for
4 papers · 1 filter
Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples
Kanghyun Choi, Deokki Hong, Noseong Park +2
Model quantization is known as a promising method to compress deep neural networks, especially for inferences on lightweight mobile or edge devices. However, model quantization usu…
An Attention Module for Convolutional Neural Networks
Zhu Baozhou, Peter Hofstee, Jinho Lee +1
Attention mechanism has been regarded as an advanced technique to capture long-range feature interactions and to boost the representation capability for convolutional neural networ…
AutoReCon: Neural Architecture Search-based Reconstruction for Data-free Compression
Baozhou Zhu, Peter Hofstee, Johan Peltenburg +2
Data-free compression raises a new challenge because the original training dataset for a pre-trained model to be compressed is not available due to privacy or transmission issues.…
GradPIM: A Practical Processing-in-DRAM Architecture for Gradient Descent
Heesu Kim, Hanmin Park, Taehyun Kim +6
In this paper, we present GradPIM, a processing-in-memory architecture which accelerates parameter updates of deep neural networks training. As one of processing-in-memory techniqu…