From 44c3cfed7e74ee32dd1b1f1e4cdaf49ad1cf2def Mon Sep 17 00:00:00 2001 From: majianjia Date: Mon, 29 Nov 2021 22:52:57 +0000 Subject: [PATCH] Add performance comparison --- README.md | 27 ++++++++++++++++-- ...rformance-comparison-tflite-cubeai-eai.png | Bin 0 -> 57727 bytes .../performance-comparison-tflite-cubeai.png | Bin 0 -> 138425 bytes 3 files changed, 24 insertions(+), 3 deletions(-) create mode 100644 docs/figures/performance-comparison-tflite-cubeai-eai.png create mode 100644 docs/figures/performance-comparison-tflite-cubeai.png diff --git a/README.md b/README.md index 99276f4d..4a24636e 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,8 @@ NNoM is a high-level inference Neural Network library specifically for microcont - Support complex structures; Inception, ResNet, DenseNet, Octave Convolution... - User-friendly interfaces. - High-performance backend selections. -- Onboard (MCU) evaluation tools; Runtime analysis, Top-k, Confusion matrix... +- Onboard pre-compiling - zero interpreter performance loss at runtime. +- Onboard evaluation tools; Runtime analysis, Top-k, Confusion matrix... The structure of NNoM is shown below: ![](docs/figures/nnom_structure.png) @@ -64,8 +65,7 @@ However, the available NN libs for MCU are too low-level which make it sooooo di Therefore, we build NNoM to help embedded developers for faster and simpler deploying NN model directly to MCU. > NNoM will manage the strucutre, memory and everything else for the developer. All you need to do is feeding your new measurements and getting the results. -**NNoM is now working closely with Keras (You can easily learn [**Keras**](https://keras.io/) in 30 seconds!).** -There is no need to learn TensorFlow/Lite or other libs. + ## Documentations @@ -82,6 +82,27 @@ There is no need to learn TensorFlow/Lite or other libs. [RT-Thread-MNIST example (Chinese)](docs/example_mnist_simple_cn.md) + + +## Performance + +There are many articles compared NNoM with other famous MCU AI tools, such as TensorFlow LiteSTM32Cube.AI .etc. + +**Raphael Zingg etc** from Zurich University of Applied Sciences compare nnom with tflite, cube, and e-Ai in their paper ["Artificial Intelligence on Microcontrollers"](https://github.com/InES-HPMM/Artificial_Intelligence_on_Microcontrollers/blob/master/Artificial_Intelligence_on_Microcontrollers.pdf) blog https://blog.zhaw.ch/high-performance/2020/05/14/artificial-intelligence-on-microcontrollers/ + +![performance-comparison-tflite-cubeai-eai](docs/figures/performance-comparison-tflite-cubeai-eai.png) + +**Butt Usman Ali** from POLITECNICO DI TORINO, did below comparison in [the thesis: On the deployment of Artificial Neural Networks (ANN) in low +cost embedded systems](https://webthesis.biblio.polito.it/19692/1/tesi.pdf) + +![performance-comparison-tflite-cubeai](docs/figures/performance-comparison-tflite-cubeai.png) + +Both articles shows that NNoM is not only comparable with other popular NN framework but with faster inference time and sometime less memory footprint. + +**Note:** These graphs and tables are credited to their authors. Please refer the their original papers for details and copyright. + + + ## Examples **Documented examples** diff --git a/docs/figures/performance-comparison-tflite-cubeai-eai.png b/docs/figures/performance-comparison-tflite-cubeai-eai.png new file mode 100644 index 0000000000000000000000000000000000000000..3df35f7ffbdcdd9131de947630e8a38c9c925a7d GIT binary patch literal 57727 zcmeF2Wmj8Kx2P!uFWw@>g1Z-YX|N*2tvG}hm*Vd3?i4HTR*GA2E$&dHNRZ%m()XNm z?|-=Ceo3;|$k==Axz_Y&MyM*wVqv_)KtMpil9!WFLqGuN!he^cp};@SB(~opAOI2M zWh6D+vya!1)0?!v@olOTEvp4=zj1SucW_IiwFc1Bku^1DJ|Ia`Et(m;~|11AjZ2#{{TeIa8`!LS#-hicGnTFkmKcrOCZmU@>z99zn|wa%dbUD zF;VT5QZY8+oO@ZRKh%_!hl^q^Bepvi^!f>Kx8C*y_C4_O%QZ_RizDa3@mcxfz5fK4 z)V@K7Q%yF3z>sj&pIjD6%Kewl``!`GBA=ahxe!-vKz zVu;JvX9LpbOYxV>V_EV0>Ep?l$4M|zYd5kh>TqCj-}6!B%AHgQA}5N_eX`Jl@`vnM z%N)fmzfTvdLdiDTs9B8iJdvrvcJ1eVl+U$G2c+v~p^9yO1+`jom9!u3N59p0t+911 z+;rY_EE&CA8MTOc{+!LRbG@I<>!f3U#(sPm;P7?4X7IaUXb^ww6JMR#*CiA?k5Cz_ zc5SK{uXYGwVSV`Yu5oDNGXJyzsrN>a|yun8P)F$)wxSnX!K;bkfEb- 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