2 papers
cs.LG2025
Enabling Unstructured Sparse Acceleration on Structured Sparse Accelerators
Geonhwa Jeong, Po-An Tsai, Abhimanyu R. Bambhaniya +2
Exploiting sparsity in deep neural networks (DNNs) has been a promising area for meeting the growing computation requirements. To minimize the overhead of sparse acceleration, hard…
cs.AR2025
MicroScopiQ: Accelerating Foundational Models through Outlier-Aware Microscaling Quantization
Akshat Ramachandran, Souvik Kundu, Tushar Krishna
Quantization of foundational models (FMs) is significantly more challenging than traditional DNNs due to the emergence of large magnitude values called outliers. Existing outlier-a…