12 citations · 25 across the 7 of their papers we have counts for
7 papers
MxGLUT: A Reconfigurable LUT-Centric Broadcast Dataflow Accelerator for Mixed-Precision GEMM
Weiyu Zhou, Chen Ding, Mingyuan Liu +7
Large language model (LLM) inference suffers from growing inefficiency across the prefill and decode phases, especially under weight-only quantization, where activations remain in…
Motion Guided Token Compression for Efficient Masked Video Modeling
Yukun Feng, Yangming Shi, Fengze Liu +1
Recent developments in Transformers have achieved notable strides in enhancing video comprehension. Nonetheless, the O() computation complexity associated with attention mecha…
Memory Augmented Lookup Dictionary based Language Modeling for Automatic Speech Recognition
Yukun Feng, Ming Tu, Rui Xia +2
Recent studies have shown that using an external Language Model (LM) benefits the end-to-end Automatic Speech Recognition (ASR). However, predicting tokens that appear less frequen…
Automatic Document Selection for Efficient Encoder Pretraining
Yukun Feng, Patrick Xia, Benjamin Van Durme +1
Building pretrained language models is considered expensive and data-intensive, but must we increase dataset size to achieve better performance? We propose an alternative to larger…
Learn To Remember: Transformer with Recurrent Memory for Document-Level Machine Translation
Yukun Feng, Feng Li, Ziang Song +2
The Transformer architecture has led to significant gains in machine translation. However, most studies focus on only sentence-level translation without considering the context dep…
The NLP Task Effectiveness of Long-Range Transformers
Guanghui Qin, Yukun Feng, Benjamin Van Durme
Transformer models cannot easily scale to long sequences due to their O(N^2) time and space complexity. This has led to Transformer variants seeking to lower computational complexi…
Spiral Language Modeling
Yong Cao, Yukun Feng, Shaohui Kuang +1
In almost all text generation applications, word sequences are constructed in a left-to-right (L2R) or right-to-left (R2L) manner, as natural language sentences are written either…