====== Edge Computing for Neural Networks ======
Sometimes called tiny machine learning (TinyML) which is machine learning for resourced constrained devices.
===== Overviews =====
  * [[https://arxiv.org/pdf/1910.10231.pdf|Voghoei et al 2019 - Deep Learning at the Edge]]
  * [[https://ieeexplore.ieee.org/document/8763885|2019 - Deep Learning With Edge Computing: A Review]]
  * [[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/2311.11883|2023 - Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review]]

===== 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/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 =====
  * [[Efficient NNs]]
  * [[Model Compression]]