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[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning

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MCUNet: Tiny Deep Learning on IoT Devices

This is the official implementation of the MCUNet series.

demo

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Overview

Microcontrollers are low-cost, low-power hardware. They are widely deployed and have wide applications.

teaser

But the tight memory budget (50,000x smaller than GPUs) makes deep learning deployment difficult.

teaser

MCUNet is a system-algorithm co-design framework for tiny deep learning on microcontrollers. It consists of TinyNAS and TinyEngine. They are co-designed to fit the tight memory budgets.

With system-algorithm co-design, we can significantly improve the deep learning performance on the same tiny memory budget.

teaser

Our TinyEngine inference engine could be a useful infrastructure for MCU-based AI applications. It significantly improves the inference speed and reduces the memory usage compared to existing libraries like TF-Lite Micro, CMSIS-NN, MicroTVM, etc. It improves the inference speed by 1.5-3x, and reduces the peak memory by 2.7-4.8x.

teaser

Model Zoo

Usage

You can build the pre-trained PyTorch fp32 model or the int8 quantized model in TF-Lite format.

from mcunet.model_zoo import net_id_list, build_model, download_tflite
print(net_id_list)  # the list of models in the model zoo

# pytorch fp32 model
model, image_size, description = build_model(net_id="mcunet-320kB", pretrained=True)  # you can replace net_id with any other option from net_id_list

# download tflite file to tflite_path
tflite_path = download_tflite(net_id="mcunet-320kB")

Evaluate

To evaluate the accuracy of PyTorch fp32 models, run:

python eval_torch.py --net_id mcunet-320kB --dataset {imagenet/vww} --data-dir PATH/TO/DATA/val

To evaluate the accuracy of TF-Lite int8 models, run:

python eval_tflite.py --net_id mcunet-320kB --dataset {imagenet/vww} --data-dir PATH/TO/DATA/val

Model List

  • Note that all the latency, SRAM, and Flash usage are profiled with TinyEngine on STM32F746.
  • Here we only provide the int8 quantized modes. int4 quantized models (as shown in the paper) can further push the accuracy-memory trade-off, but lacking a general format support.
  • For accuracy (top1, top-5), we report the accuracy of fp32/int8 models respectively

The ImageNet model list:

net_id MACs #Params SRAM Flash Top-1
(fp32/int8)
Top-5
(fp32/int8)
# baseline models
mbv2-320kB 23.5M 0.75M 308kB 862kB 49.7%/49.0% 74.6%/73.8%
proxyless-320kB 38.3M 0.75M 292kB 892kB 57.0%/56.2% 80.2%/79.7%
# mcunet models
mcunet-10fps 6.4M 0.75M 266kB 889kB 41.5%/40.4% 66.3%/65.2%
mcunet-5fps 12.8M 0.64M 307kB 992kB 51.5%/49.9% 75.5%/74.1%
mcunet-256kB 67.3M 0.73M 242kB 878kB 60.9%/60.3% 83.3%/82.6%
mcunet-320kB 81.8M 0.74M 293kB 897kB 62.2%/61.8% 84.5%/84.2%
mcunet-512kB 125.9M 1.73M 456kB 1876kB 68.4%/68.0% 88.4%/88.1%

The VWW model list:

Note that the VWW dataset might be hard to prepare. You can download our pre-built minival set from here, around 380MB.

net_id MACs #Params SRAM Flash Top-1
(fp32/int8)
mcunet-10fps-vww 6.0M 0.37M 146kB 617kB 87.4%/87.3%
mcunet-5fps-vww 11.6M 0.43M 162kB 689kB 88.9%/88.9%
mcunet-320kB-vww 55.8M 0.64M 311kB 897kB 91.7%/91.8%

For TF-Lite int8 models, we do not use quantization-aware training (QAT), so some results is slightly lower than paper numbers.

Detection Model

We also share the person detection model used in the demo. To visualize the model's prediction on a sample image, please run the following command:

python eval_det.py

It will visualize the prediction here: assets/sample_images/person_det_vis.jpg.

The model takes in a small input resolution of 128x160 to reduce memory usage. It does not achieve state-of-the-art performance due to the limited image and model size but should provide decent performance for tinyML applications (please check the demo for a video recording). We will also release the deployment code in the upcoming TinyEngine release.

Requirement

  • Python 3.6+

  • PyTorch 1.4.0+

  • Tensorflow 1.15 (if you want to test TF-Lite models; CPU support only)

Acknowledgement

We thank MIT-IBM Watson AI Lab, Intel, Amazon, SONY, Qualcomm, NSF for supporting this research.

Citation

If you find the project helpful, please consider citing our paper:

@article{lin2020mcunet,
  title={Mcunet: Tiny deep learning on iot devices},
  author={Lin, Ji and Chen, Wei-Ming and Lin, Yujun and Gan, Chuang and Han, Song},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

@inproceedings{
  lin2021mcunetv2,
  title={MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning},
  author={Lin, Ji and Chen, Wei-Ming and Cai, Han and Gan, Chuang and Han, Song},
  booktitle={Annual Conference on Neural Information Processing Systems (NeurIPS)},
  year={2021}
} 

Related Projects

On-Device Training Under 256KB Memory

TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning (NeurIPS'20)

Once for All: Train One Network and Specialize it for Efficient Deployment (ICLR'20)

ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware (ICLR'19)

AutoML for Architecting Efficient and Specialized Neural Networks (IEEE Micro)

AMC: AutoML for Model Compression and Acceleration on Mobile Devices (ECCV'18)

HAQ: Hardware-Aware Automated Quantization (CVPR'19, oral)

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[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning

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