4 citations · 11 across the 9 of their papers we have counts for
5 papers · 1 filter
SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs
Yizhao Gao, Zhichen Zeng, Dayou Du +8
Attention is the cornerstone of modern Large Language Models (LLMs). Yet its quadratic complexity hinders efficiency and scalability, especially for long-context processing. A prom…
STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs
Peijie Dong, Lujun Li, Yuedong Zhong +8
In this paper, we present the first structural binarization method for LLM compression to less than 1-bit precision. Although LLMs have achieved remarkable performance, their memor…
Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey
Dayou Du, Gu Gong, Xiaowen Chu
Vision Transformers (ViTs) have recently garnered considerable attention, emerging as a promising alternative to convolutional neural networks (CNNs) in several vision-related appl…
Benchmarking and Dissecting the Nvidia Hopper GPU Architecture
Weile Luo, Ruibo Fan, Zeyu Li +3
Graphics processing units (GPUs) are continually evolving to cater to the computational demands of contemporary general-purpose workloads, particularly those driven by artificial i…
BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation
Dayou Du, Yijia Zhang, Shijie Cao +4
The upscaling of Large Language Models (LLMs) has yielded impressive advances in natural language processing, yet it also poses significant deployment challenges. Weight quantizati…