Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Embedded and mobile deep learning research resources
| Date | Stars |
|---|---|
| 2026-07-31 | 769 |
| 2026-08-03 | 769 |
| 2026-08-06 | 769 |
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growth rate 0.00%/day
# Awesome EMDL Embedded and mobile deep learning research notes. ## Papers ### Survey 1. [EfficientDNNs](https://github.com/MingSun-Tse/EfficientDNNs) [Repo] 1. [Awesome ML Model Compression](https://github.com/cedrickchee/awesome-ml-model-compression) [Repo] 1. [TinyML Papers and Projects](https://github.com/gigwegbe/tinyml-papers-and-projects) [Repo] 1. [TinyML Platforms Benchmarking](https://arxiv.org/abs/2112.01319) [arXiv '21] 1. [TinyML: A Systematic Review and Synthesis of Existing Research](https://ieeexplore.ieee.org/abstract/document/9722636) [ICAIIC '21] 1. [TinyML Meets IoT: A Comprehensive Survey](https://www.sciencedirect.com/science/article/abs/pii/S2542660521001025) [Internet of Things '21] 1. [A review on TinyML: State-of-the-art and prospects](https://www.sciencedirect.com/science/article/pii/S1319157821003335) [Journal of King Saud Univ. '21] 1. [TinyML Benchmark: Executing Fully Connected Neural Networks on Commodity Microcontrollers](https://aran.library.nuigalway.ie/handle/10379/16770) [IEEE '21] 1. [Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better](https://arxiv.org/abs/2106.08962) [arXiv '21] 1. [Benchmarking TinyML Systems: Challenges and Direction](https://arxiv.org/abs/2003.04821) [arXiv '20] 1. [Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey](https://ieeexplore.ieee.org/abstract/document/9043731) [IEEE '20] 1. [The Deep Learning Compiler: A Comprehensive Survey](https://arxiv.org/abs/2002.03794) [arXiv '20] 1. [Recent Advances in Efficient Computation of Deep Convolutional Neural Networks](https://arxiv.org/abs/1802.00939) [arXiv '18] 1. [A Survey of Model Compression and Acceleration for Deep Neural Networks](https://arxiv.org/abs/1710.09282) [arXiv '17] ### Model 1. [EtinyNet: Extremely Tiny Network for TinyML](https://www.aaai.org/AAAI22Papers/AAAI-4889.XuK.pdf) [AAAI '21] 1. [MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning](https://arxiv.org/abs/2110.15352) [NeurIPS '21, MIT] 1. [SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems](https://proceedings.mlsys.org/papers/2020/86) [MLSys '20, IBM] 1. [Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets](https://arxiv.org/abs/2010.14819) [NeurIPS '20, Huawei] 1. [MCUNet: Tiny Deep Learning on IoT Devices](https://arxiv.org/abs/2007.10319) [NeurIPS '20, MIT] 1. [GhostNet: More Features from Cheap Operations](https://arxiv.org/abs/1911.11907) [CVPR '20, Huawei] 1. [MicroNet for Efficient Language Modeling](https://arxiv.org/abs/2005.07877) [NeurIPS '19, MIT] 1. [Searching for MobileNetV3](https://arxiv.org/abs/1905.02244) [ICCV '19, Google] 1. [MobilenetV2: Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation](https://arxiv.org/pdf/1801.04381.pdf) [CVPR '18, Google] 1. [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://arxiv.org/abs/1812.00332) [arXiv '18, MIT] 1. [DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices](https://arxiv.org/abs/1708.04728) [AAAI'18, Samsung] 1. [NasNet: Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/pdf/1707.07012.pdf) [arXiv '17, Google] 1. [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://arxiv.org/abs/1707.01083) [arXiv '17, Megvii] 1. [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) [arXiv '17, Google] 1. [CondenseNet: An Efficient DenseNet using Learned Group Convolutions](https://arxiv.org/abs/1711.09224) [arXiv '17] ### System 1. [BSC: Block-based Stochastic Computing to Enable Accurate and Efficient TinyML](https://arxiv.org/pdf/2111.06686.pdf?ref=https://githubhelp.com) [ASP-DAC '22] 1. [CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPG
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SangwooChoi · AWS · Canada
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Santiago Castro · @Netflix · United States
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daquexian · Skywork AI · China
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Zhmin Zhao · Software Analysis and Intelligence Lab (SAIL) & Lab on Maintenance, Construction and Intelligence of Software (MCIS) · Canada
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eMHa · TB Geoscience · Indonesia
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d20be56cd2989186, topic:quantization, topic:pruning, readme:model compression
matched fp:d20be56cd2989186, topic:inference
matched fp:d20be56cd2989186, topic:deep-learning