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ECML PKDD 2021 ML meets IoT Tutorial Part I: ML for IoT Devices

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Bharath Sudharsan
Bharath Sudharsan

Tutorial Website: https://sites.google.com/view/ml-meets-iot-ecml-tutorial/

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Machine Learning Meets Internet of Things: From Theory to Practice Part I: ML for IoT Devices Bharath Sudharsan ECML PKDD 2021 Tutorial
IoT Edge Computing Valuable extractable knowledge at the edge ▪ Cisco annual Internet report ✓ By 2023, there will be more than three times more networked devices on Earth than humans ✓ About half of the global connections will be machine-to-machine (M2M) connections ▪ Push applications, data and computing power to the edge of the Internet ▪ Deploying, managing apps outside the data centers, on the customer-owned devices
IoT Edge Computing - Hardware View ARM Cortex-M0 MCU based BLE beacon Edge gateway with GPUs and SSDs Powerful CPU + basic GPU based SBCs (single board computers) Edge computing hardware: highly resource constrained -> high resource (left to right) ▪ MCUs and small CPUs: BLE beacons, smart bulbs, smart plugs, TV remotes, fitness bands ▪ SBCs: Raspberry Pis, BeagleBones, NVIDIA Jetsons, Latte Pandas, Intel NUCs, Google Coral ▪ GPU accelerated: AWS snowball, Digi gateways, Dell Edge Gateways for IoT, HPE Edgeline ▪ Why MCU? Roughly 50 billion MCU chips were shipped in 2020 (market estimates), which far exceeds other chips like GPUs & CPUs (only 100 million units sold)
IoT Edge Computing - Hardware Spec ▪ No file system, Lack Python support (only C, C++), only few hundred MHz clock speed, low SRAM and Flash memory ▪ No parallel execution units), lack inbuilt hardware accelerators such as APU (Accelerated Processing Unit), KPU (convolution operation accelerator), FPU (Floating-point accelerator), and FFT (Fourier transform accelerator) Popular open-source MCU boards with specification
TensorFlow Lite for Microcontrollers
TensorFlow Hub ▪ Models ready to use from off the shelf ▪ Optimizers applicable: Quantization, Pruning, etc. ▪ Can add additional tunable layers at the network end
Netron NN Model Visualizer ▪ Demo: Download popular models and visualize and brief analysis using https://netron.app/ ✓ yolo-v5-tflite from https://tfhub.dev/neso613/lite-model/yolo-v5-tflite/tflite_model/1 ✓ 3 types of fully connected Neural Networks (NNs) from https://github.com/bharathsudharsan/TinyML-Benchmark-NNs-on-MCUs FC 1 x 10: 1 layer with 10 neurons FC 10 + 50: 2 layers, where 1st layer contains 10 neurons, and 2nd layer contains 50 neurons FC 10 x 10: 10 layers, where each layer contains 10 neurons
Arduino IDE + TF Lite Library ▪ Install the Arduino TensorFlow Lite library ▪ Run the Arduino TF Lite hello world example ✓ Files -> Examples -> Arduino_TensorFlowLite -> hello_world. The example sketch should appear ✓ Upload on MCU, open the Serial Plotter, observe LED ✓ Model trained to replicate sine function. Generates data pattern to either blink LEDs or control an animation
Dev Boards - Hardware View ▪ Pinout summary ✓ Power ✓ Logic ✓ I2C & SPI ✓ GPIO ✓ Analog
Dev Boards - Hardware View ▪ ST-LINK V2 programmer provides a more reliable way to flash the Blue Pill boards ▪ Full range of STM32 SWD interface debugging ▪ The tiniest Arduino ATtiny85 is flashed (programmed burned) using UART IC
Dev Boards - ML Applications https://cartesiam.ai/product/
Dev Boards - ML Applications
Dev Boards - ML Applications https://www.edgeimpulse.com/blog
Conclusion ▪ A small device with an enormous impact on our lives. For examples ✓ TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids ✓ A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints
Contact: Bharath Sudharsan Email: bharath.sudharsan@insight-centre.org www.confirm.ie

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ECML PKDD 2021 ML meets IoT Tutorial Part I: ML for IoT Devices

  • 1. Machine Learning Meets Internet of Things: From Theory to Practice Part I: ML for IoT Devices Bharath Sudharsan ECML PKDD 2021 Tutorial
  • 2. IoT Edge Computing Valuable extractable knowledge at the edge ▪ Cisco annual Internet report ✓ By 2023, there will be more than three times more networked devices on Earth than humans ✓ About half of the global connections will be machine-to-machine (M2M) connections ▪ Push applications, data and computing power to the edge of the Internet ▪ Deploying, managing apps outside the data centers, on the customer-owned devices
  • 3. IoT Edge Computing - Hardware View ARM Cortex-M0 MCU based BLE beacon Edge gateway with GPUs and SSDs Powerful CPU + basic GPU based SBCs (single board computers) Edge computing hardware: highly resource constrained -> high resource (left to right) ▪ MCUs and small CPUs: BLE beacons, smart bulbs, smart plugs, TV remotes, fitness bands ▪ SBCs: Raspberry Pis, BeagleBones, NVIDIA Jetsons, Latte Pandas, Intel NUCs, Google Coral ▪ GPU accelerated: AWS snowball, Digi gateways, Dell Edge Gateways for IoT, HPE Edgeline ▪ Why MCU? Roughly 50 billion MCU chips were shipped in 2020 (market estimates), which far exceeds other chips like GPUs & CPUs (only 100 million units sold)
  • 4. IoT Edge Computing - Hardware Spec ▪ No file system, Lack Python support (only C, C++), only few hundred MHz clock speed, low SRAM and Flash memory ▪ No parallel execution units), lack inbuilt hardware accelerators such as APU (Accelerated Processing Unit), KPU (convolution operation accelerator), FPU (Floating-point accelerator), and FFT (Fourier transform accelerator) Popular open-source MCU boards with specification
  • 5. TensorFlow Lite for Microcontrollers
  • 6. TensorFlow Hub ▪ Models ready to use from off the shelf ▪ Optimizers applicable: Quantization, Pruning, etc. ▪ Can add additional tunable layers at the network end
  • 7. Netron NN Model Visualizer ▪ Demo: Download popular models and visualize and brief analysis using https://netron.app/ ✓ yolo-v5-tflite from https://tfhub.dev/neso613/lite-model/yolo-v5-tflite/tflite_model/1 ✓ 3 types of fully connected Neural Networks (NNs) from https://github.com/bharathsudharsan/TinyML-Benchmark-NNs-on-MCUs FC 1 x 10: 1 layer with 10 neurons FC 10 + 50: 2 layers, where 1st layer contains 10 neurons, and 2nd layer contains 50 neurons FC 10 x 10: 10 layers, where each layer contains 10 neurons
  • 8. Arduino IDE + TF Lite Library ▪ Install the Arduino TensorFlow Lite library ▪ Run the Arduino TF Lite hello world example ✓ Files -> Examples -> Arduino_TensorFlowLite -> hello_world. The example sketch should appear ✓ Upload on MCU, open the Serial Plotter, observe LED ✓ Model trained to replicate sine function. Generates data pattern to either blink LEDs or control an animation
  • 9. Dev Boards - Hardware View ▪ Pinout summary ✓ Power ✓ Logic ✓ I2C & SPI ✓ GPIO ✓ Analog
  • 10. Dev Boards - Hardware View ▪ ST-LINK V2 programmer provides a more reliable way to flash the Blue Pill boards ▪ Full range of STM32 SWD interface debugging ▪ The tiniest Arduino ATtiny85 is flashed (programmed burned) using UART IC
  • 11. Dev Boards - ML Applications https://cartesiam.ai/product/
  • 12. Dev Boards - ML Applications
  • 13. Dev Boards - ML Applications https://www.edgeimpulse.com/blog
  • 14. Conclusion ▪ A small device with an enormous impact on our lives. For examples ✓ TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids ✓ A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints
  • 15. Contact: Bharath Sudharsan Email: bharath.sudharsan@insight-centre.org www.confirm.ie