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Yun Liang

4 papers hereh-index 394.6k citations108 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV1
  • cs.DC1
  • cs.LG1
  • cs.PL1
same name
  • Yun Liang — 7 papers
  • Yun Liang — 4 papers
  • Yun Liang — 2 papers
  • Yun Liang — 2 papers, h 12
  • Yun Liang — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182020
most citedREQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs

15 citations · 17 across the 2 of their papers we have counts for

collaborators

4 papers

cs.PL2020★ 2 cited

Systolic Computing on GPUs for Productive Performance

Hongbo Rong, Xiaochen Hao, Yun Liang +3

We propose a language and compiler to productively build high-performance {\it software systolic arrays} that run on GPUs. Based on a rigorous mathematical foundation (uniform recu…

cs.CV2019★ 15 cited

REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs

Caiwen Ding, Shuo Wang, Ning Liu +3

Deep neural networks (DNNs), as the basis of object detection, will play a key role in the development of future autonomous systems with full autonomy. The autonomous systems have…

cs.LG2018

C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs

Shuo Wang, Zhe Li, Caiwen Ding +4

Recently, significant accuracy improvement has been achieved for acoustic recognition systems by increasing the model size of Long Short-Term Memory (LSTM) networks. Unfortunately,…

cs.DC2018

CuLDA_CGS: Solving Large-scale LDA Problems on GPUs

Xiaolong Xie, Yun Liang, Xiuhong Li +1

Latent Dirichlet Allocation(LDA) is a popular topic model. Given the fact that the input corpus of LDA algorithms consists of millions to billions of tokens, the LDA training proce…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.