[D][R] Deploying deep models on memory constrained devices

• MNN

  1 8,017 8.0 C++

  MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba

  

  However, I am looking on this subject through the problem of training/finetuning deep models on the edge devices, being increasingly available thing to do. Looking at tflite, alibaba's MNN, mit-han-lab's tinyengine etc..

• tinyengine

  2 646 0.0 C

  [NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning; [NeurIPS 2022] MCUNetV3: On-Device Training Under 256KB Memory (by mit-han-lab)

  

  However, I am looking on this subject through the problem of training/finetuning deep models on the edge devices, being increasingly available thing to do. Looking at tflite, alibaba's MNN, mit-han-lab's tinyengine etc..

• InfluxDB

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