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cs.AR2025
NVR: Vector Runahead on NPUs for Sparse Memory Access
Hui Wang, Zhengpeng Zhao, Jing Wang +11
Deep Neural Networks are increasingly leveraging sparsity to reduce the scaling up of model parameter size. However, reducing wall-clock time through sparsity and pruning remains c…
cs.AR2025
Pushing the Limits of BFP on Narrow Precision LLM Inference
Hui Wang, Yuan Cheng, Xiaomeng Han +3
The substantial computational and memory demands of Large Language Models (LLMs) hinder their deployment. Block Floating Point (BFP) has proven effective in accelerating linear ope…
cs.LG2025
OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting
Xing Hu, Yuan Cheng, Dawei Yang +6
Post-training quantization (PTQ) has emerged as a widely adopted technique for compressing and accelerating Large Language Models (LLMs). The major challenge in LLM quantization is…