2 citations · 6 across the 13 of their papers we have counts for
5 papers · 1 filter
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-…
ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution
Liu Yang, Zeyu Nie, Andrew Liu +4
The transition from sequential to parallel computing is essential for modern high-performance applications but is hindered by the steep learning curve of concurrent programming. Th…
The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws
Tian Jin, Ahmed Imtiaz Humayun, Utku Evci +4
Pruning eliminates unnecessary parameters in neural networks; it offers a promising solution to the growing computational demands of large language models (LLMs). While many focus…
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…
Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers
Abhimanyu Rajeshkumar Bambhaniya, Amir Yazdanbakhsh, Suvinay Subramanian +4
N:M Structured sparsity has garnered significant interest as a result of relatively modest overhead and improved efficiency. Additionally, this form of sparsity holds considerable…