4 papers
QwT-v2: Practical, Effective and Efficient Post-Training Quantization
Ningyuan Tang, Minghao Fu, Hao Yu +1
Network quantization is arguably one of the most practical network compression approaches for reducing the enormous resource consumption of modern deep neural networks. They usuall…
Quantization without Tears
Minghao Fu, Hao Yu, Jie Shao +3
Deep neural networks, while achieving remarkable success across diverse tasks, demand significant resources, including computation, GPU memory, bandwidth, storage, and energy. Netw…
Effectively Compress KV Heads for LLM
Hao Yu, Zelan Yang, Shen Li +2
The advent of pre-trained large language models (LLMs) has revolutionized various natural language processing tasks. These models predominantly employ an auto-regressive decoding m…
Unified Low-rank Compression Framework for Click-through Rate Prediction
Hao Yu, Minghao Fu, Jiandong Ding +2
Deep Click-Through Rate (CTR) prediction models play an important role in modern industrial recommendation scenarios. However, high memory overhead and computational costs limit th…