3 papers
cs.LG2026
MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference
Simla Burcu Harma, Danila Mishin, Zhengyuan Su +7
4-bit quantization enables efficient LLM inference, but suffers from significant accuracy degradation due to outliers. Prior work addresses this problem via data rotation or mixed-…
cs.AR2025
EONSim: An NPU Simulator for On-Chip Memory and Embedding Vector Operations
Sangun Choi, Yunho Oh
Embedding vector operations are a key component of modern deep neural network workloads. Unlike matrix operations with deterministic access patterns, embedding vector operations ex…
cs.LG2025
Effective Interplay between Sparsity and Quantization: From Theory to Practice
Simla Burcu Harma, Ayan Chakraborty, Elizaveta Kostenok +8
The increasing size of deep neural networks (DNNs) necessitates effective model compression to reduce their computational and memory footprints. Sparsity and quantization are two p…