5 citations · 9 across the 11 of their papers we have counts for
12 papers
PID-Comm: A Fast and Flexible Collective Communication Framework for Commodity Processing-in-DIMM Devices
Si Ung Noh, Junguk Hong, Chaemin Lim +5
Recent dual in-line memory modules (DIMMs) are starting to support processing-in-memory (PIM) by associating their memory banks with processing elements (PEs), allowing application…
Smart-Infinity: Fast Large Language Model Training using Near-Storage Processing on a Real System
Hongsun Jang, Jaeyong Song, Jaewon Jung +3
The recent huge advance of Large Language Models (LLMs) is mainly driven by the increase in the number of parameters. This has led to substantial memory capacity requirements, nece…
AGAThA: Fast and Efficient GPU Acceleration of Guided Sequence Alignment for Long Read Mapping
Seongyeon Park, Junguk Hong, Jaeyong Song +3
With the advance in genome sequencing technology, the lengths of deoxyribonucleic acid (DNA) sequencing results are rapidly increasing at lower prices than ever. However, the longe…
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…
SGCN: Exploiting Compressed-Sparse Features in Deep Graph Convolutional Network Accelerators
Mingi Yoo, Jaeyong Song, Jounghoo Lee +3
Graph convolutional networks (GCNs) are becoming increasingly popular as they overcome the limited applicability of prior neural networks. A GCN takes as input an arbitrarily struc…
Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication Compression
Jaeyong Song, Jinkyu Yim, Jaewon Jung +4
In training of modern large natural language processing (NLP) models, it has become a common practice to split models using 3D parallelism to multiple GPUs. Such technique, however…
Slice-and-Forge: Making Better Use of Caches for Graph Convolutional Network Accelerators
Mingi Yoo, Jaeyong Song, Hyeyoon Lee +4
Graph convolutional networks (GCNs) are becoming increasingly popular as they can process a wide variety of data formats that prior deep neural networks cannot easily support. One…
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…
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…
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…
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…
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…