4 citations · 4 across the 7 of their papers we have counts for
8 papers
Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs
Pierre Abillama, Changwoo Lee, Juechu Dong +3
Recent advances in transformer-based foundation models have made them the default choice for many tasks, but their rapidly growing size makes fitting a full model on a single GPU i…
MonarchAttention: Zero-Shot Conversion to Fast, Hardware-Aware Structured Attention
Can Yaras, Alec S. Xu, Pierre Abillama +2
Transformers have achieved state-of-the-art performance across various tasks, but suffer from a notable quadratic complexity in sequence length due to the attention mechanism. In t…
BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference
Changwoo Lee, Soo Min Kwon, Qing Qu +1
Large-scale foundation models have demonstrated exceptional performance in language and vision tasks. However, the numerous dense matrix-vector operations involved in these large n…
Differentiable Learning of Generalized Structured Matrices for Efficient Deep Neural Networks
Changwoo Lee, Hun-Seok Kim
This paper investigates efficient deep neural networks (DNNs) to replace dense unstructured weight matrices with structured ones that possess desired properties. The challenge aris…
Deep Joint Source-Channel Coding with Iterative Source Error Correction
Changwoo Lee, Xiao Hu, Hun-Seok Kim
In this paper, we propose an iterative source error correction (ISEC) decoding scheme for deep-learning-based joint source-channel coding (Deep JSCC). Given a noisy codeword receiv…
Learning-Based Near-Orthogonal Superposition Code for MIMO Short Message Transmission
Chenghong Bian, Chin-Wei Hsu, Changwoo Lee +1
Massive machine type communication (mMTC) has attracted new coding schemes optimized for reliable short message transmission. In this paper, a novel deep learning-based near-orthog…
Unified Signal Compression Using a GAN with Iterative Latent Representation Optimization
Bowen Liu, Changwoo Lee, Ang Cao +1
We propose a unified signal compression framework that uses a generative adversarial network (GAN) to compress heterogeneous signals. The compressed signal is represented as a late…
A Confidence-Calibrated MOBA Game Winner Predictor
Dong-Hee Kim, Changwoo Lee, Ki-Seok Chung
In this paper, we propose a confidence-calibration method for predicting the winner of a famous multiplayer online battle arena (MOBA) game, League of Legends. In MOBA games, the d…