readme: ---
  license: cc-by-nc-4.0
  ---
  
  # **Our Mission for embedded AI**
  
  In our mission to unleash AI on the edge, Datakalab has applied its proprietary quantization method on classical model architectures and converted everything to run in .rtm format for seamless compatibility with NXP®  hardware, such as **I.MX 8M Plus®**  and others.
  
  Download any of the quantised models below to put your deep learning models on NXP® hardware.  Enjoy and let us know what you think!
  
  If you are looking for  further compression methods (continual learning, pruning, batch norm folding, etc.) to fit your models on tiny devices, we’d love to hear from you!
  Contact us at  
  
  # **Latency on NXP®  NPU with less than 1% of accuracy drop**
  
  | **Model Name**  |  <p align="center"> **Latency on NXP® NPU** |  <p align="center"> **Memory Size Ratio [ fp32/int8 ]**
  | ------------- | ------------- | ------------- |
  | Mobilenet V2  | <p align="center"> **6.98 ms** <p>  | <p align="center">**x 3.6** |
  | Resnet50  | <p align="center">**19.07 ms**  | <p align="center">**x 3.9** | 
  | Efficientnetlite b0 | <p align="center">**7.54 ms**  | <p align="center">**x 3.7** | 
  | YoloV3  | <p align="center">**129 ms**  | <p align="center">**x 3.75** <p> | 
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  # **Known issue**
  
  Quantized Gemm is not runnable with the rtm format, we will need to change those into Matmul and Add operations
  
  # **Thanks**
  All those benchmarks where made using NXP® eiQ<sup>TM</sup> Portal! Thanks a lot NXP®
  
  # **Contact us**
  
  Feel free to reach out by email at **hello at datakalab.com**