---
title: "Hardware and Architecture — papers, page 2"
description: "Hardware and Architecture: 36 papers of the Elamur Research library · page 2 of 2 · 26–36, the most cited first — our summary of each, its ideas…"
canonical: "https://research.elamur.ai/free/en/shelves/computer-science/hardware-and-architecture/page/2/"
---
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# 🔌 Hardware and Architecture

36 papers · page 2 of 2 · 26–36 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/)

Hardware and Architecture is a subfield of Computer Science in the OpenAlex classification of science. The topics most of its papers here are on: «Parallel Computing and Optimization Techniques», «Energy Efficiency in Computing», «Embedded Systems Design Techniques».

## Papers

[← prev](https://md.research.elamur.ai/free/en/shelves/computer-science/hardware-and-architecture/) [1](https://md.research.elamur.ai/free/en/shelves/computer-science/hardware-and-architecture/) **2**

26. [**KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache**Zirui Liu, Jiayi Yuan, Hongye Jin, et al.*2024 · 243 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-kivi-tuning-free-asymmetric-2bit/)
27. [**Mesh-TensorFlow: Deep Learning for Supercomputers**Noam Shazeer, Youlong Cheng, Niki Parmar, et al.*2018 · 182 citations*](https://md.research.elamur.ai/free/en/papers/shazeer-2018-mesh-tensorflow-deep-learning-supercomputers/)
28. [**KernelBench: Can LLMs Write Efficient GPU Kernels?**Anne Ouyang, Simon Guo, Simran Arora, et al.*2025 · 170 citations*](https://md.research.elamur.ai/free/en/papers/ouyang-2025-kernelbench-llms-write-efficient-gpu/)
29. [**DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models**Sam Ade Jacobs, Masahiro Tanaka, Chengming Zhang, et al.*2023 · 159 citations*](https://md.research.elamur.ai/free/en/papers/jacobs-2023-deepspeed-ulysses-system-optimizations-enabling/)
30. [**Sequence Parallelism: Long Sequence Training from System Perspective**Shenggui Li, Fuzhao Xue, Chaitanya Baranwal, et al.*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-sequence-parallelism-long-sequence-training/)
31. [**Break the Sequential Dependency of LLM Inference Using Lookahead Decoding**Yichao Fu, Peter Bailis, Ion Stoica, et al.*2024 · 112 citations*](https://md.research.elamur.ai/free/en/papers/fu-2024-break-sequential-dependency-llm-inference/)
32. [**Resolving Discrepancies in Compute-Optimal Scaling of Language Models**Tomer Porian, Mitchell Wortsman, Jenia Jitsev, et al.*2024 · 14 citations*](https://md.research.elamur.ai/free/en/papers/porian-2024-resolving-discrepancies-compute-optimal-scaling/)
33. [**Chinchilla Scaling: A replication attempt**Tamay Besiroglu, Ege Erdil, Matthew Barnett, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/besiroglu-2024-chinchilla-scaling-replication-attempt/)
34. [**Recovered in Translation: Efficient Pipeline for Automated Translation of Benchmarks and Datasets**Hanna Yukhymenko, Anton Alexandrov, Martin Vechev*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/yukhymenko-2026-recovered-translation-efficient-pipeline-automated/)
35. [**Diagnosing Translated Benchmarks: An Automated Quality Assurance Study of the EU20 Benchmark Suite**Klaudia Thellmann, Bernhard Stadler, Michael Färber*2026*](https://md.research.elamur.ai/free/en/papers/thellmann-2026-diagnosing-translated-benchmarks-automated-quality/)
36. [**The Mark Fades: Adaptive Evolutionary Paraphrase-based Attack against LLM Watermarks**Yusheng Zhao, Jian Zhao, Tianle Zhang, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zhao-2026-mark-fades-adaptive-evolutionary-paraphrase/)

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