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Ligeng Zhu

Ligeng Zhu has 2 papers in our library, in 2018–2020, most of them on GPU & parallel computing; often with Han Cai, Song Han, Chuang Gan.

2 papers in our library · 2018–2020

OpenAlex · h-index 16 · 39 works · 2,823 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 364 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #3215

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Massachusetts Institute of TechnologyCambridge · United States2018–2025 · 15 papers🏢NvidiaSanta Clara · United States2024–2026 · 7 papers🏢NvidiaReading · United Kingdom2024–2026 · 5 papers🎓Simon Fraser UniversityBurnaby · Canada2018 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 1Research subjects and languages the work is about.GPU & parallel computing · 2 works

 Works by year

2020s

20201 paper

2010s

20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware (2018 · 479 citations)
    Neural architecture search typically costs thousands of GPU hours. Differentiable variants such as DARTS and One-Shot are cheaper, but they keep the activations of every candidate operation in memory, so memory grows with the number of choices. As a result, earlier methods search on cheaper stand-ins, such as smaller…
  2. HAT: Hardware-Aware Transformers for Efficient Natural Language Processing (2020 · 294 citations)
    Transformer models for machine translation are too slow on constrained devices. For example, Transformer-Big needs about 13G FLOPs and roughly 20 seconds on a Raspberry Pi to translate a 30-word sentence. The authors make two observations: FLOPs poorly predict real latency, and the architecture that runs fastest…

All works

6 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. ProxylessNAS: Direct Neural Architecture Search on Target Task and HardwarearXiv:1812.00332Han Cai, Ligeng Zhu, Song Han2018 · 479 citations
  2. HAT: Hardware-Aware Transformers for Efficient Natural Language ProcessingAnnual Meeting of the Association for Computational Linguistics (ACL)Hanrui Wang, Zhanghao Wu, Zhijian Liu, et al.2020 · 294 citations
  3. NVILA: Efficient Frontier Visual Language Models ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetZhijian Liu et al.2025 · 73 citations
  4. LongVILA: Scaling Long-Context Visual Language Models for Long Videos ↗arXiv:2408.10188 · no summary yetYukang Chen et al.2024 · 49 citations
  5. Wolf: Dense Video Captioning with a World Summarization Framework ↗arXiv (Cornell University) · no summary yetBoyi Li et al.2024 · 6 citations
  6. FoundationMotion: Auto-Labeling and Reasoning about Spatial Movement in Videos ↗arXiv (Cornell University) · no summary yetGan, Yulu et al.2025 · 1 citation

 Co-authors here

6 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 364 times (of the 149,439 papers whose bibliographies we read); 22 citing papers with a page here →.

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