4 papers
DyRA: Dynamic Residual Approximation for Efficient Matrix Multiplication in DNNs
Daewon Chae, Hyunwon Chung, Changwoo Lee +1
Large-scale foundation models achieve strong performance across diverse tasks, but their size makes inference costly, largely due to dense matrix multiplications. Prior work reduce…
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…
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…