6.5k citations · 18.4k across the 52 of their papers we have counts for
5 papers · 1 filter
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
Guangxuan Xiao, Ji Lin, Mickael Seznec +3
Large language models (LLMs) show excellent performance but are compute- and memory-intensive. Quantization can reduce memory and accelerate inference. However, existing methods ca…
HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
Hanrui Wang, Zhanghao Wu, Zhijian Liu +4
Transformers are ubiquitous in Natural Language Processing (NLP) tasks, but they are difficult to be deployed on hardware due to the intensive computation. To enable low-latency in…
MicroNet for Efficient Language Modeling
Zhongxia Yan, Hanrui Wang, Demi Guo +1
It is important to design compact language models for efficient deployment. We improve upon recent advances in both the language modeling domain and the model-compression domain to…
Lite Transformer with Long-Short Range Attention
Zhanghao Wu, Zhijian Liu, Ji Lin +2
Transformer has become ubiquitous in natural language processing (e.g., machine translation, question answering); however, it requires enormous amount of computations to achieve hi…
ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA
Song Han, Junlong Kang, Huizi Mao +9
Long Short-Term Memory (LSTM) is widely used in speech recognition. In order to achieve higher prediction accuracy, machine learning scientists have built larger and larger models.…