6 citations · 24 across the 8 of their papers we have counts for
9 papers
SaLoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs
Seongyeon Park, Hajin Kim, Tanveer Ahmad +5
Sequence alignment forms an important backbone in many sequencing applications. A commonly used strategy for sequence alignment is an approximate string matching with a two-dimensi…
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…
Deep Composer Classification Using Symbolic Representation
Sunghyeon Kim, Hyeyoon Lee, Sunjong Park +2
In this study, we train deep neural networks to classify composer on a symbolic domain. The model takes a two-channel two-dimensional input, i.e., onset and note activations of tim…
SoFAr: Shortcut-based Fractal Architectures for Binary Convolutional Neural Networks
Zhu Baozhou, Peter Hofstee, Jinho Lee +1
Binary Convolutional Neural Networks (BCNNs) can significantly improve the efficiency of Deep Convolutional Neural Networks (DCNNs) for their deployment on resource-constrained pla…
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…
System G Distributed Graph Database
Gabriel Tanase, Toyotaro Suzumura, Jinho Lee +5
Motivated by the need to extract knowledge and value from interconnected data, graph analytics on big data is a very active area of research in both industry and academia. To suppo…