{"id":4614,"url":"https://github.com/csarron/awesome-emdl","name":"awesome-emdl","description":"Embedded and mobile deep learning research resources","projects_count":147,"last_synced_at":"2026-08-30T01:00:57.013Z","repository":{"id":38022023,"uuid":"93459704","full_name":"csarron/awesome-emdl","owner":"csarron","description":"Embedded and mobile deep learning research resources","archived":false,"fork":false,"pushed_at":"2023-03-14T18:09:02.000Z","size":91,"stargazers_count":769,"open_issues_count":1,"forks_count":168,"subscribers_count":82,"default_branch":"master","last_synced_at":"2026-08-09T16:54:40.691Z","etag":null,"topics":["deep-learning","deep-neural-networks","efficient-neural-networks","embedded-ai","inference","mobile-ai","mobile-deep-learning","mobile-inference","neural-network-compression","pruning","quantization"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/csarron.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2017-06-06T00:42:48.000Z","updated_at":"2026-07-28T15:44:43.000Z","dependencies_parsed_at":"2022-07-18T08:13:40.554Z","dependency_job_id":"7ca01563-8e34-492f-bc20-68ebcf2151c5","html_url":"https://github.com/csarron/awesome-emdl","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/csarron/awesome-emdl","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csarron%2Fawesome-emdl","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csarron%2Fawesome-emdl/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csarron%2Fawesome-emdl/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csarron%2Fawesome-emdl/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/csarron","download_url":"https://codeload.github.com/csarron/awesome-emdl/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csarron%2Fawesome-emdl/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":36485666,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-08-06T04:43:03.162Z","status":"online","status_checked_at":"2026-08-10T02:00:05.122Z","response_time":58,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-06T20:25:00.171Z","updated_at":"2026-08-30T01:00:57.013Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Libraries","Papers","Tutorials","General","Courses","Tools","Related Repos"],"sub_categories":["Inference Framework","Model","Pruning","Quantization","Survey","Approximation","System","General","Characterization","Web","Edge / Tiny MLOps","NEON","OpenCL","GPU","Optimization Tools","Driver","Vulkan","RenderScript","Research Demos"],"readme":"# Awesome EMDL\n\nEmbedded and mobile deep learning research notes.\n\n## Papers\n\n### Survey\n\n1. [EfficientDNNs](https://github.com/MingSun-Tse/EfficientDNNs) [Repo]\n1. [Awesome ML Model Compression](https://github.com/cedrickchee/awesome-ml-model-compression) [Repo]\n1. [TinyML Papers and Projects](https://github.com/gigwegbe/tinyml-papers-and-projects) [Repo]\n1. [TinyML Platforms Benchmarking](https://arxiv.org/abs/2112.01319) [arXiv '21]\n1. [TinyML: A Systematic Review and Synthesis of Existing Research](https://ieeexplore.ieee.org/abstract/document/9722636) [ICAIIC '21]\n1. [TinyML Meets IoT: A Comprehensive Survey](https://www.sciencedirect.com/science/article/abs/pii/S2542660521001025) [Internet of Things '21]\n1. [A review on TinyML: State-of-the-art and prospects](https://www.sciencedirect.com/science/article/pii/S1319157821003335) [Journal of King Saud Univ. '21]\n1. [TinyML Benchmark: Executing Fully Connected Neural Networks on Commodity Microcontrollers](https://aran.library.nuigalway.ie/handle/10379/16770) [IEEE '21]\n1. [Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better](https://arxiv.org/abs/2106.08962) [arXiv '21]\n1. [Benchmarking TinyML Systems: Challenges and Direction](https://arxiv.org/abs/2003.04821) [arXiv '20]\n1. [Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey](https://ieeexplore.ieee.org/abstract/document/9043731) [IEEE '20]\n1. [The Deep Learning Compiler: A Comprehensive Survey](https://arxiv.org/abs/2002.03794) [arXiv '20]\n1. [Recent Advances in Efficient Computation of Deep Convolutional Neural Networks](https://arxiv.org/abs/1802.00939) [arXiv '18]\n1. [A Survey of Model Compression and Acceleration for Deep Neural Networks](https://arxiv.org/abs/1710.09282) [arXiv '17]\n\n### Model\n\n1. [EtinyNet: Extremely Tiny Network for TinyML](https://www.aaai.org/AAAI22Papers/AAAI-4889.XuK.pdf) [AAAI '21]\n1. [MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning](https://arxiv.org/abs/2110.15352) [NeurIPS '21, MIT]\n1. [SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems](https://proceedings.mlsys.org/papers/2020/86) [MLSys '20, IBM]\n1. [Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets](https://arxiv.org/abs/2010.14819) [NeurIPS '20, Huawei]\n1. [MCUNet: Tiny Deep Learning on IoT Devices](https://arxiv.org/abs/2007.10319) [NeurIPS '20, MIT]\n1. [GhostNet: More Features from Cheap Operations](https://arxiv.org/abs/1911.11907) [CVPR '20, Huawei]\n1. [MicroNet for Efficient Language Modeling](https://arxiv.org/abs/2005.07877) [NeurIPS '19, MIT]\n1. [Searching for MobileNetV3](https://arxiv.org/abs/1905.02244) [ICCV '19, Google]\n1. [MobilenetV2: Inverted Residuals and Linear Bottlenecks: Mobile Networks for\nClassification, Detection and Segmentation](https://arxiv.org/pdf/1801.04381.pdf) [CVPR '18, Google]\n1. [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://arxiv.org/abs/1812.00332) [arXiv '18, MIT]\n1. [DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices](https://arxiv.org/abs/1708.04728) [AAAI'18, Samsung]\n1. [NasNet: Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/pdf/1707.07012.pdf) [arXiv '17, Google]\n1. [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://arxiv.org/abs/1707.01083) [arXiv '17, Megvii]\n1. [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) [arXiv '17, Google]\n1. [CondenseNet: An Efficient DenseNet using Learned Group Convolutions](https://arxiv.org/abs/1711.09224) [arXiv '17]\n\n### System\n\n1. [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]\n1. [CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs](https://arxiv.org/abs/2201.01863) [arXiv '22, Google]\n1. [UDC: Unified DNAS for Compressible TinyML Models](https://arxiv.org/abs/2201.05842) [arXiv '22, Arm]\n1. [AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator](https://arxiv.org/abs/2111.06503) [arXiv '21, Arm]\n1. [TinyTL: Reduce Activations, Not Trainable Parameters for Efficient On-Device Learning](https://arxiv.org/abs/2007.11622) [NeurIPS '20, MIT]\n1. [Once for All: Train One Network and Specialize it for Efficient Deployment](https://arxiv.org/abs/1908.09791) [ICLR '20, MIT]\n1. [DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications](https://www.sigmobile.org/mobisys/2017/accepted.php) [MobiSys '17]\n1. [DeepEye: Resource Efficient Local Execution of Multiple Deep Vision Models using Wearable Commodity Hardware](http://fahim-kawsar.net/papers/Mathur.MobiSys2017-Camera.pdf) [MobiSys '17]\n1. [MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU](https://arxiv.org/abs/1706.00878) [EMDL '17]\n1. [fpgaConvNet: A Toolflow for Mapping Diverse Convolutional Neural Networks on Embedded FPGAs](https://arxiv.org/abs/1711.08740) [NIPS '17]\n1. [DeepSense: A GPU-based deep convolutional neural network framework on commodity mobile devices](http://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4278\u0026context=sis_research) [WearSys '16]\n1. [DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices](http://niclane.org/pubs/deepx_ipsn.pdf) [IPSN '16]\n1. [EIE: Efficient Inference Engine on Compressed Deep Neural Network](https://arxiv.org/abs/1602.01528) [ISCA '16]\n1. [MCDNN: An Approximation-Based Execution Framework for Deep Stream Processing Under Resource Constraints](http://haneul.github.io/papers/mcdnn.pdf) [MobiSys '16]\n1. [DXTK: Enabling Resource-efficient Deep Learning on Mobile and Embedded Devices with the DeepX Toolkit](http://niclane.org/pubs/dxtk_mobicase.pdf) [MobiCASE '16]\n1. [Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables](http://niclane.org/pubs/sparsesep_sensys.pdf) [SenSys ’16]\n1. [An Early Resource Characterization of Deep Learning on Wearables, Smartphones and Internet-of-Things Devices](http://niclane.org/pubs/iotapp15_early.pdf) [IoT-App ’15]\n1. [CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android](https://arxiv.org/abs/1511.07376) [MM '16]\n\n### Quantization\n\n1. [Quantizing deep convolutional networks for efficient inference: A whitepaper](https://arxiv.org/abs/1806.08342) [arXiv '18]\n1. [LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks](https://arxiv.org/pdf/1807.10029.pdf) [ECCV'18]\n1. [Training and Inference with Integers in Deep Neural Networks](https://openreview.net/forum?id=HJGXzmspb) [ICLR'18]\n1. [The ZipML Framework for Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning](https://arxiv.org/abs/1611.05402) [ICML'17]\n1. [Loss-aware Binarization of Deep Networks](https://arxiv.org/abs/1611.01600) [ICLR'17]\n1. [Towards the Limit of Network Quantization](https://arxiv.org/abs/1612.01543) [ICLR'17]\n1. [Deep Learning with Low Precision by Half-wave Gaussian Quantization](https://arxiv.org/abs/1702.00953) [CVPR'17]\n1. [ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks](https://arxiv.org/abs/1706.02393) [arXiv'17]\n1. [Quantized Convolutional Neural Networks for Mobile Devices](https://arxiv.org/abs/1512.06473) [CVPR '16]\n1. [Fixed-Point Performance Analysis of Recurrent Neural Networks](https://arxiv.org/abs/1512.01322) [ICASSP'16]\n1. [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://arxiv.org/abs/1609.07061) [arXiv'16]\n1. [Compressing Deep Convolutional Networks using Vector Quantization](https://arxiv.org/abs/1412.6115) [arXiv'14]\n\n### Pruning\n\n1. [Awesome-Pruning](https://github.com/he-y/Awesome-Pruning) [Repo]\n1. [Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration](https://arxiv.org/abs/1811.00250) [CVPR'19]\n1. [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://arxiv.org/abs/1710.01878) [ICLR'18]\n1. [Pruning Filters for Efficient ConvNets](https://arxiv.org/abs/1608.08710) [ICLR'17]\n1. [Pruning Convolutional Neural Networks for Resource Efficient Inference](https://arxiv.org/abs/1611.06440) [ICLR'17]\n1. [Soft Weight-Sharing for Neural Network Compression](https://arxiv.org/abs/1702.04008) [ICLR'17]\n1. [Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning](https://arxiv.org/abs/1611.05128) [CVPR'17]\n1. [ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression](https://arxiv.org/abs/1707.06342) [ICCV'17]\n1. [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://arxiv.org/abs/1510.00149) [ICLR'16]\n1. [Dynamic Network Surgery for Efficient DNNs](https://arxiv.org/abs/1608.04493) [NIPS'16]\n1. [Learning both Weights and Connections for Efficient Neural Networks](https://arxiv.org/abs/1506.02626) [NIPS'15]\n\n### Approximation\n\n1. [High performance ultra-low-precision convolutions on mobile devices](https://arxiv.org/abs/1712.02427) [NIPS'17]\n1. [Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications](https://arxiv.org/abs/1511.06530) [ICLR'16]\n1. [Efficient and Accurate Approximations of Nonlinear Convolutional Networks](https://arxiv.org/abs/1411.4229) [CVPR'15]\n1. [Accelerating Very Deep Convolutional Networks for Classification and Detection](https://arxiv.org/abs/1505.06798) (Extended version of above one)\n1. [Convolutional neural networks with low-rank regularization](https://arxiv.org/abs/1511.06067) [arXiv'15]\n1. [Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation](https://arxiv.org/abs/1404.0736) [NIPS'14]\n\n### Characterization\n\n1. [A First Look at Deep Learning Apps on Smartphones](https://arxiv.org/abs/1812.05448) [WWW'19]\n1.  [Machine Learning at Facebook: Understanding Inference at the Edge](https://research.fb.com/publications/machine-learning-at-facebook-understanding-inference-at-the-edge/) [HPCA'19]\n1. [NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications](https://arxiv.org/abs/1804.03230) [ECCV 2018]\n1. [Latency and Throughput Characterization of Convolutional Neural Networks for Mobile Computer Vision](https://arxiv.org/abs/1803.09492) [MMSys’18]\n\n## Libraries\n\n### Inference Framework\n\n1. [Alibaba - MNN](https://github.com/alibaba/MNN) - is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba.\n1. [Apple - CoreML](https://developer.apple.com/documentation/coreml) - is integrate machine learning models into your app. [BERT and GPT-2 on iPhone](https://github.com/huggingface/swift-coreml-transformers)\n1. [Arm - ComputeLibrary](https://github.com/ARM-software/ComputeLibrary) - is a set of computer vision and machine learning functions optimised for both Arm CPUs and GPUs using SIMD technologies. [Intro](https://developer.arm.com/technologies/compute-library)\n1. [Arm - Arm NN](https://github.com/ARM-software/armnn) - is the most performant machine learning (ML) inference engine for Android and Linux, accelerating ML on Arm Cortex-A CPUs and Arm Mali GPUs.\n1. [Baidu - Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite) - is multi-platform high performance deep learning inference engine.\n1. [DeepLearningKit](https://github.com/DeepLearningKit/DeepLearningKit) - is Open Source Deep Learning Framework for Apple's iOS, OS X and tvOS.\n1. [Edge Impulse](https://edgeimpulse.com) - Interactive platform to generate models that can run in microcontrollers. They are also quite active on social netwoks talking about recent news on EdgeAI/TinyML.\n1. [Google - TensorFlow Lite](https://www.tensorflow.org/lite/performance/gpu) - is an open source deep learning framework for on-device inference.\n1. [Intel - OpenVINO](https://github.com/openvinotoolkit/openvino) - Comprehensive toolkit to optimize your processes for faster inference.\n1. [JDAI Computer Vision - dabnn](https://github.com/JDAI-CV/dabnn) - is an accelerated binary neural networks inference framework for mobile platform.\n1. [Meta - PyTorch Mobile](https://pytorch.org/mobile/home) - is a new framework for helping mobile developers and machine learning engineers embed PyTorch ML models on-device.\n1. [Microsoft - DeepSpeed](https://github.com/microsoft/DeepSpeed) - is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.\n1. [Microsoft - ELL](https://github.com/Microsoft/ELL) - allows you to design and deploy intelligent machine-learned models onto resource constrained platforms and small single-board computers, like Raspberry Pi, Arduino, and micro:bit.\n1. [Microsoft - ONNX RUntime](https://github.com/microsoft/onnxruntime) - cross-platform, high performance ML inferencing and training accelerator.\n1. [Nvidia - TensorRT](https://github.com/NVIDIA/TensorRT) - is a C++ library for high performance inference on NVIDIA GPUs and deep learning accelerators.\n1. [OAID - Tengine](https://github.com/OAID/Tengine) - is a lite, high performance, modular inference engine for embedded device\n1. [Qualcomm - Neural Processing SDK for AI](https://developer.qualcomm.com/software/qualcomm-neural-processing-sdk) - Libraries to developers run NN models on Snapdragon mobile platforms taking advantage of the CPU, GPU and/or DSP.\n1. [Tencent - ncnn](https://github.com/Tencent/ncnn) - is a high-performance neural network inference framework optimized for the mobile platform.\n1. [uTensor](https://github.com/uTensor/uTensor) - AI inference library based on mbed (an RTOS for ARM chipsets) and TensorFlow.\n1. [XiaoMi - Mace](https://github.com/XiaoMi/mace) - is a deep learning inference framework optimized for mobile heterogeneous computing platforms. \n1. [xmartlabs - Bender](https://github.com/xmartlabs/Bender) - Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.\n\n### Optimization Tools\n\n1. [Neural Network Distiller](https://github.com/NervanaSystems/distiller) -  Python package for neural network compression research.\n1. [PocketFlow](https://github.com/Tencent/PocketFlow) - An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.\n\n### Research Demos\n\n1. [RSTensorFlow](https://nesl.github.io/RSTensorFlow) - GPU Accelerated TensorFlow for Commodity Android Devices.\n\n### Web\n\n1. [mil-tokyo/webdnn](https://github.com/mil-tokyo/webdnn) - Fastest DNN Execution Framework on Web Browser.\n\n## General\n\n1. [Caffe2 AICamera](https://github.com/bwasti/AICamera)\n1. [TensorFlow Android Camera Demo](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android)\n1. [TensorFlow iOS Example](https://github.com/hollance/TensorFlow-iOS-Example)\n1. [TensorFlow OpenMV Camera Module](https://github.com/openmv/openmv)\n\n\n### Edge / Tiny MLOps\n\n1. [Tiny-MLOps: a framework for orchestrating ML applications at the far edge of IoT systems](https://ieeexplore.ieee.org/abstract/document/9787703/authors#authors) [EAIS '22]\n1. [MLOps for TinyML: Challenges \u0026 Directions in Operationalizing TinyML at Scale](https://cms.tinyml.org/wp-content/uploads/talks2022/tinyML_Talks_Vijay_Janapa_Reddi_220524.pdf) [TinyML Talks '22]\n1. [TinyMLOps: Operational Challenges for Widespread Edge AI Adoption](https://arxiv.org/pdf/2203.10923.pdf) [arXiv '22]\n1. [A TinyMLaaS Ecosystem for Machine Learning in IoT: Overview and Research Challenges](https://ieeexplore.ieee.org/document/9427352) [VLSI-DAT '21]\n1. [SOLIS: The MLOps journey from data acquisition to actionable insights](https://arxiv.org/abs/2112.11925) [arXiv '21]\n1. [Edge MLOps: An Automation Framework for AIoT Applications](https://www.computer.org/csdl/proceedings-article/ic2e/2021/497000a191/1yJZ8cHPTkQ) [IC2E '21]\n1. [SensiX++: Bringing MLOPs and Multi-tenant Model Serving to Sensory Edge Devices](https://arxiv.org/abs/2109.03947) [arXiv '21, Nokia]\n\n### Vulkan\n\n1. [Vulkan API Examples and Demos](https://github.com/SaschaWillems/Vulkan)\n1. [Neural Machine Translation on Android](https://github.com/harvardnlp/nmt-android)\n\n### OpenCL\n\n1. [DeepMon](https://github.com/JC1DA/DeepMon)\n\n### RenderScript\n\n1. [Mobile_ConvNet: RenderScript CNN for Android](https://github.com/mtmd/Mobile_ConvNet)\n\n## Tutorials\n\n### General\n\n1. [Squeezing Deep Learning Into Mobile Phones](https://www.slideshare.net/anirudhkoul/squeezing-deep-learning-into-mobile-phones)\n1. [Deep Learning – Tutorial and Recent Trends](https://www.dropbox.com/s/p7lvelt0aihrwtl/FPGA%2717%20tutorial%20Song%20Han.pdf?dl=0)\n1. [Tutorial on Hardware Architectures for Deep Neural Networks](http://eyeriss.mit.edu/tutorial.html)\n1. [Efficient Convolutional Neural Network Inference on Mobile GPUs](https://www.slideshare.net/embeddedvision/efficient-convolutional-neural-network-inference-on-mobile-gpus-a-presentation-from-imagination-technologies)\n\n### NEON\n\n1. [NEON™ Programmer’s Guide](https://developer.arm.com/docs/den0018/latest/neontm-version-10-programmers-guide)\n\n### OpenCL\n\n1. [ARM® Mali™ GPU OpenCL Developer Guide](http://infocenter.arm.com/help/index.jsp?topic=/com.arm.doc.100614_0303_00_en/ada1432742770595.html), [pdf](http://infocenter.arm.com/help/topic/com.arm.doc.100614_0303_00_en/arm_mali_gpu_opencl_developer_guide_100614_0303_00_en.pdf)\n1. [Optimal Compute on ARM Mali™ GPUs](http://www.cs.bris.ac.uk/home/simonm/montblanc/OpenCL_on_Mali.pdf)\n1. [GPU Compute for Mobile Devices](http://www.iwocl.org/wp-content/uploads/iwocl-2014-workshop-Tim-Hartley.pdf)\n1. [Compute for Mobile Devices Performance focused](http://kesen.realtimerendering.com/Compute_for_Mobile_Devices5.pdf)\n1. [Hands On OpenCL](https://handsonopencl.github.io/)\n1. [Adreno OpenCL Programming Guide](https://developer.qualcomm.com/download/adrenosdk/adreno-opencl-programming-guide.pdf)\n1. [Better OpenCL Performance on Qualcomm Adreno GPU](https://developer.qualcomm.com/blog/better-opencl-performance-qualcomm-adreno-gpu-memory-optimization)\n\n## Courses\n\n1. [UW Deep learning **systems**](http://dlsys.cs.washington.edu/schedule)\n1. [Berkeley Machine Learning Systems](https://ucbrise.github.io/cs294-ai-sys-fa19/)\n\n\n## Tools\n\n### GPU\n\n1. [Bifrost GPU architecture and ARM Mali-G71 GPU](https://www.hotchips.org/wp-content/uploads/hc_archives/hc28/HC28.22-Monday-Epub/HC28.22.10-GPU-HPC-Epub/HC28.22.110-Bifrost-JemDavies-ARM-v04-9.pdf)\n1. [Midgard GPU Architecture](http://malideveloper.arm.com/downloads/ARM_Game_Developer_Days/PDFs/2-Mali-GPU-architecture-overview-and-tile-local-storage.pdf), [ARM Mali-T880 GPU](https://www.hotchips.org/wp-content/uploads/hc_archives/hc27/HC27.25-Tuesday-Epub/HC27.25.50-GPU-Epub/HC27.25.531-Mali-T880-Bratt-ARM-2015_08_23.pdf)\n1. [Mobile GPU market share](https://hwstats.unity3d.com/mobile/gpu.html)\n\n### Driver\n\n1. [Adreno] [csarron/qcom_vendor_binaries: Common Proprietary Qualcomm Binaries](https://github.com/csarron/qcom_vendor_binaries)\n1. [Mali] [Fevax/vendor_samsung_hero2ltexx: Blobs from s7 Edge G935F](https://github.com/Fevax/vendor_samsung_hero2ltexx)\n\n\n## Related Repos\n+ [EfficientDNNs](https://github.com/MingSun-Tse/EfficientDNNs) by @MingSun-Tse ![GitHub stars](https://img.shields.io/github/stars/MingSun-Tse/EfficientDNNs?style=social) ![GitHub last commit](https://img.shields.io/github/last-commit/MingSun-Tse/EfficientDNNs.svg)\n+ [Awesome ML Model Compression](https://github.com/cedrickchee/awesome-ml-model-compression) by @cedrickchee ![GitHub stars](https://img.shields.io/github/stars/cedrickchee/awesome-ml-model-compression?style=social) ![GitHub last commit](https://img.shields.io/github/last-commit/cedrickchee/awesome-ml-model-compression.svg)\n+ [Awesome Pruning](https://github.com/he-y/Awesome-Pruning) by @he-y ![GitHub stars](https://img.shields.io/github/stars/cedrickchee/awesome-ml-model-compression?style=social) ![GitHub last commit](https://img.shields.io/github/last-commit/cedrickchee/awesome-ml-model-compression.svg)\n+ [Model Compression](https://github.com/j-marple-dev/model_compression) by @j-marple-dev ![GitHub stars](https://img.shields.io/github/stars/j-marple-dev/model_compression?style=social) ![GitHub last commit](https://img.shields.io/github/last-commit/j-marple-dev/model_compression.svg)\n+ [awesome-AutoML-and-Lightweight-Models](https://github.com/guan-yuan/awesome-AutoML-and-Lightweight-Models) by @guan-yuan ![GitHub stars](https://img.shields.io/github/stars/guan-yuan/awesome-AutoML-and-Lightweight-Models?style=social) ![GitHub last commit](https://img.shields.io/github/last-commit/guan-yuan/awesome-AutoML-and-Lightweight-Models.svg)\n+ [knowledge-distillation-papers](https://github.com/lhyfst/knowledge-distillation-papers) by @lhyfst ![GitHub stars](https://img.shields.io/github/stars/lhyfst/knowledge-distillation-papers?style=social) ![GitHub last commit](https://img.shields.io/github/last-commit/lhyfst/knowledge-distillation-papers.svg)\n+ [Awesome-model-compression-and-acceleration](https://github.com/memoiry/Awesome-model-compression-and-acceleration) by @memoiry ![GitHub stars](https://img.shields.io/github/stars/memoiry/Awesome-model-compression-and-acceleration?style=social) ![GitHub last commit](https://img.shields.io/github/last-commit/memoiry/Awesome-model-compression-and-acceleration.svg)\n+ [Embedded Neural Network](https://github.com/ZhishengWang/Embedded-Neural-Network) by @ZhishengWang ![GitHub stars](https://img.shields.io/github/stars/ZhishengWang/Embedded-Neural-Network?style=social) ![GitHub last commit](https://img.shields.io/github/last-commit/ZhishengWang/Embedded-Neural-Network.svg)\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/csarron%2Fawesome-emdl/projects"}