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Hanrui Wang

Hanrui Wang has 2 papers in our library, in 2020, most of them on GPU & parallel computing, Efficient models & compression; often with Song Han, Chuang Gan, Han Cai.

2 papers in our library · 2020

OpenAlex · ORCID · h-index 22 · 87 works · 4,644 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 192 times in our library · ⭐ Paper Stars ranking: #3072

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 · 43 papers🎓University of California, Los AngelesLos Angeles · United States2018–2026 · 9 papers🎓Fudan UniversityShanghai · China2016 · 2 papers🔬State Key Laboratory of ASIC and SystemShanghai2016 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 more institutions on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 worksHardware and Architecture · 1 work

🧭 Topics 2Research subjects and languages the work is about.Efficient models & compression · 1 workGPU & parallel computing · 1 work

 Works by year

2020s

20202 papers

 Papers

The most cited first (OpenAlex and our library).

  1. SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning (2020 · 534 citations)
    Attention-based NLP models such as BERT and GPT-2 run slowly on CPUs and GPUs. The cost grows quadratically with input length, much of the time goes to moving data rather than arithmetic, and existing accelerators were built for CNNs and RNNs. SpAtten (HPCA 2021) is an algorithm-hardware co-design that cuts both…
  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…

 Co-authors here

7 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 192 times (of the 149,439 papers whose bibliographies we read); 13 citing papers with a page here →.

Updated

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