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ml:edge_computing [2021/03/11 18:23] – created jmflanigml:edge_computing [2025/03/26 20:13] (current) – [Related Pages] jmflanig
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 ====== Edge Computing for Neural Networks ====== ====== Edge Computing for Neural Networks ======
 +Sometimes called tiny machine learning (TinyML) which is machine learning for resourced constrained devices.
 ===== Overviews ===== ===== Overviews =====
   * [[https://arxiv.org/pdf/1910.10231.pdf|Voghoei et al 2019 - Deep Learning at the Edge]]   * [[https://arxiv.org/pdf/1910.10231.pdf|Voghoei et al 2019 - Deep Learning at the Edge]]
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   * [[https://arxiv.org/pdf/1907.08349.pdf|Wang et al 2019 - Convergence of Edge Computing and Deep Learning: A Comprehensive Survey]]   * [[https://arxiv.org/pdf/1907.08349.pdf|Wang et al 2019 - Convergence of Edge Computing and Deep Learning: A Comprehensive Survey]]
   * [[https://arxiv.org/pdf/2003.12172.pdf|Xu et al 2020 - Edge Intelligence: Architectures, Challenges, and Applications]]   * [[https://arxiv.org/pdf/2003.12172.pdf|Xu et al 2020 - Edge Intelligence: Architectures, Challenges, and Applications]]
 +  * [[https://arxiv.org/pdf/2311.11883|2023 - Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review]]
  
 ===== Papers ===== ===== Papers =====
   * [[https://arxiv.org/pdf/2006.11316.pdf|Iandola et al 2020 - SqueezeBERT: What can computer vision teach NLP about efficient neural networks?]]   * [[https://arxiv.org/pdf/2006.11316.pdf|Iandola et al 2020 - SqueezeBERT: What can computer vision teach NLP about efficient neural networks?]]
   * [[https://arxiv.org/pdf/1712.05877.pdf|2017 - Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference]]   * [[https://arxiv.org/pdf/1712.05877.pdf|2017 - Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference]]
 +  * [[https://arxiv.org/pdf/2004.05801.pdf|Sankar et al 2020 - ProFormer: Towards On-Device LSH Projection Based Transformers]]
 +  * [[https://www.aclweb.org/anthology/2021.eacl-main.238.pdf|Zhao et al 2021 - Extremely Small BERT Models from Mixed-Vocabulary Training]] Compresses BERT for edge devices by compressing the input word embeddings
 +
 +===== Software =====
 +  * [[https://pytorch.org/mobile/home/|PyTorch Mobile]]
 +  * [[https://www.tensorflow.org/lite|TensorFlow Lite]]
 +  * [[https://www.tensorflow.org/lite/microcontrollers|TensorFlow Lite for Microcontrollers]]
 +
 +===== Conferences and Workshops =====
 +  * [[https://mlsys.org/|MLSys]] - Has some mobile, on-device, or edge computing papers
 +  * [[https://www.tinyml.org/|TinyML]] (Educational - I don't think they publish papers)
  
 ===== Related Pages ===== ===== Related Pages =====
 +  * [[Efficient NNs]]
   * [[Model Compression]]   * [[Model Compression]]
  
  
ml/edge_computing.1615487035.txt.gz · Last modified: 2023/06/15 07:36 (external edit)
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