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Arvind Krishnamurthy

Arvind Krishnamurthy has 5 papers in our library, in 2018–2025, most of them on Language models & pretraining, Efficient models & compression; often with Luis Ceze, Tianqi Chen, Lequn Chen.

5 papers in our library · 2018–2025

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📄 5 works in our library · 🔗 Cited 902 times in our library · ⭐ Paper Stars ranking: #1582

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

 Fields, topics and concepts

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

📚 Shelves 10The field of science this work belongs to (OpenAlex fields).Computer Science · 5 worksHardware and Architecture · 4 worksComputer Vision and Pattern Recognition · 3 worksArts and Humanities · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksEngineering · 1 workComputational Theory and Mathematics · 1 workElectrical and Electronic Engineering · 1 workInformation Systems · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksEfficient models & compression · 1 work

 Works by year

2020s

20251 paper20232 papers

2010s

20191 paper20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. TVM: An Automated End-to-End Optimizing Compiler for Deep Learning (2018 · 1,384 citations)
    Deep learning models increasingly need to run on very different hardware: server GPUs, phones, embedded boards, and custom accelerators such as FPGAs and TPU-like chips.
  2. Learning to Optimize Tensor Programs (2019 · 269 citations)
    Deep learning systems depend on fast tensor operators such as matrix multiplication and convolution. Today these come mostly from hand-tuned vendor libraries like cuDNN, which cover only a narrow set of hardware and make every new device expensive to support. The authors treat operator implementation as a search…
  3. FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving (2025 · 177 citations)
    FlashInfer is an attention engine for LLM inference serving. It targets two problems: serving workloads keep changing, and attention variants and GPU architectures differ widely. Today each serving system ships its own narrow attention kernels, which is costly to maintain. FlashInfer replaces them with one…
  4. Atom: Low-bit Quantization for Efficient and Accurate LLM Serving (2023 · 156 citations)
    LLM serving relies on batching to keep GPUs busy, and quantization can further reduce memory use and compute cost.
  5. Punica: Multi-Tenant LoRA Serving (2023 · 81 citations)
    Organisations that fine-tune one base LLM with LoRA for many tenants usually end up running each adapter as a separate model.

 Co-authors here

12 of 14 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 902 times (of the 149,439 papers whose bibliographies we read); 45 citing papers with a page here →.

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