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ml:efficient_nns [2025/04/02 04:57] jmflanigml:efficient_nns [2025/05/07 06:17] (current) – [Efficient Transformers] jmflanig
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 ===== Overviews ===== ===== Overviews =====
 +  * **General**
 +    * [[https://arxiv.org/pdf/1703.09039|Sze et al 2017 - Efficient Processing of Deep Neural Networks: A Tutorial and Survey]]
   * **For LLMs**   * **For LLMs**
     * [[https://arxiv.org/pdf/2312.03863|Wan et al 2023 - Efficient Large Language Models: A Survey]]     * [[https://arxiv.org/pdf/2312.03863|Wan et al 2023 - Efficient Large Language Models: A Survey]]
     * [[https://arxiv.org/pdf/2404.14294|Zhou et al 2024 - A Survey on Efficient Inference for Large Language Models]]     * [[https://arxiv.org/pdf/2404.14294|Zhou et al 2024 - A Survey on Efficient Inference for Large Language Models]]
 +    * **Reasoning LLMs**
 +      * [[https://arxiv.org/pdf/2503.24377|Wang et al 2025 - Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models]]
  
 ===== Efficient Transformers ===== ===== Efficient Transformers =====
   * [[https://arxiv.org/pdf/2211.05102|Pope 2022 - Efficiently Scaling Transformer Inference]] Introduced the idea of the KV cache.   * [[https://arxiv.org/pdf/2211.05102|Pope 2022 - Efficiently Scaling Transformer Inference]] Introduced the idea of the KV cache.
   * [[https://arxiv.org/pdf/2311.04934|Gim et al 2023 - Prompt Cache: Modular Attention Reuse for Low-Latency Inference]]   * [[https://arxiv.org/pdf/2311.04934|Gim et al 2023 - Prompt Cache: Modular Attention Reuse for Low-Latency Inference]]
 +  * [[https://arxiv.org/pdf/2306.14048|Zhang et al 2023 - H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models]] Removes tokens from the kv-cache, and keeps the most important ones (the heavy-hitters, H2s)
  
 ===== Related Pages ===== ===== Related Pages =====
ml/efficient_nns.1743569853.txt.gz · Last modified: 2025/04/02 04:57 by jmflanig

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