aiotdevfest

Author: don

README.md

@@ -1,10 +1,12 @@
 # TinyML Application Development for Everyone
 
-<a href="https://armsummit.bemyapp.com"><img src="aiot-logo.png" alt="AIoT Dev Summit Logo"></a>
+<a href="https://aiotdevfest.org"><img src="exercises/images/AI-IOT-devfest-AZ-2020-horiz.png" alt="AIoT Devfest Logo" width="40%" style="padding-right:100px"></a>&nbsp;<a href="https://store.arduino.cc/arduino-nano-33-ble"><img src="exercises/images/nano-33-ble_iso.jpg" alt="Arduino Nano 33 BLE board" width="40%"></a>
 
-## Hands-on workshop at Arm AIoT Dev Summit December 2019
+## Hands-on workshop at AIoT Devfest January 2020
 
-In this tutorial we will teach a board to recognise gestures! We'll capture motion data from the [Arduino Nano 33 BLE Sense](https://store.arduino.cc/arduino-nano-33-ble-sense) board, import it into TensorFlow to train a model, and deploy a classifier onto the board using [TensorFlow Lite for microcontrollers](https://www.tensorflow.org/lite/microcontrollers/overview). 
+In this workshop we will teach an Arduino board to recognize gestures! We will capture motion data from the [Arduino Nano 33 BLE](https://store.arduino.cc/arduino-nano-33-ble) board<sup>[1](#note1)</sup>, import it into TensorFlow to train a model, and deploy a classifier onto the board using [TensorFlow Lite for microcontrollers](https://www.tensorflow.org/lite/microcontrollers/overview). 
+
+The hardware for this workshop has been provided by [Arduino](https://arduino.cc)
 
 ## Exercises
 
@@ -20,4 +22,15 @@ In this tutorial we will teach a board to recognise gestures! We'll capture moti
 * [Exercise 9: Gesture Controlled USB Emoji Keyboard](exercises/exercise9.md)
 * [Exercise 10: Next Steps](exercises/exercise10.md)
 
+This workshop material was developed by Sandeep Mistry and Don Coleman. 
+
+<a href="https://arduino.cc"><img src="exercises/images/Arduino_logo_R_highquality.png" alt="Arduino Logo" width=30%><a/>
+<a href="https://chariotsolutions.com"><img src="exercises/images/ChariotSolutions.png" alt="Chariot Solutions Logo" width=30%></a>
+
+Previous versions
+ * https://github.com/sandeepmistry/aimldevfest-workshop-2019
+ * https://github.com/arduino/AIoT-Dev-Summit-2019
+
+<!-- TODO link to SLIDES -->
 
+<a id="note1">1</a>: You can also use the [Arduino Nano 33 BLE Sense](https://store.arduino.cc/arduino-nano-33-ble-sense) for this workshop.

aiot-logo.png

@@ -21,14 +21,14 @@
         "colab_type": "text"
       },
       "source": [
-        "<a href=\"https://www.arduino.cc/\"><img src=\"https://raw.githubusercontent.com/sandeepmistry/aimldevfest-workshop-2019/master/images/Arduino_logo_R_highquality.png\" width=200/></a>\n",
-        "# Tiny ML on Arduino\n",
+        "<a href=\"https://www.arduino.cc/\"><img src=\"https://raw.githubusercontent.com/don/tinyml-workshop/master/exercises/images/AI-IOT-devfest-AZ-2020-horiz.png\" width=200/></a>\n",
+        "# TinyML on Arduino\n",
         "## Gesture recognition tutorial\n",
         " * Sandeep Mistry - Arduino\n",
         " * Don Coleman - Chariot Solutions\n",
         "\n",
         " \n",
-        "https://github.com/arduino/ArduinoTensorFlowLiteTutorials/"
+        "https://github.com/don/tinyml-workshop/"
       ]
     },
     {
@@ -510,7 +510,7 @@
       "source": [
         "## Encode the Model in an Arduino Header File \n",
         "\n",
-        "The next cell creates a constant byte array that contains the TFlite model. Import it as a tab with the sketch below."
+        "The next cell creates a constant byte array that contains the TFlite model. Import the generated model.h file as a tab into your Arduino sketch."
       ]
     },
     {
@@ -521,9 +521,9 @@
         "colab": {}
       },
       "source": [
-        "!echo \"const unsigned char model[] = {\" > /content/model.h\n",
-        "!cat gesture_model.tflite | xxd -i      >> /content/model.h\n",
-        "!echo \"};\"                              >> /content/model.h\n",
+        "!echo \"const unsigned char model[] __attribute__((aligned(4))) = {\"  > /content/model.h\n",
+        "!cat gesture_model.tflite | xxd -i                                  >> /content/model.h\n",
+        "!echo \"};\"                                                          >> /content/model.h\n",
         "\n",
         "import os\n",
         "model_h_size = os.path.getsize(\"model.h\")\n",

arduino_tinyml_workshop.ipynb

@@ -4,10 +4,10 @@ Now that you have things working, here are a few new things to try.
 
 - Add some more gestures to go with the :punch: and :flex:
 
-    1. Load the IMU_Capture sketch on your Arduino Nano 33 BLE Sense.
+    1. Load the IMU_Capture sketch on your Arduino Nano 33 BLE
     1. Caputure some additional gestures for a new emoji. Perhaps a :smile:, :thumbsup:, :clap:, or :wave:
     1. Load the new CSV files into your Colab model
-    1. Put the trained model back on your Nano 33 BLE Sense.
+    1. Put the trained model back on your Nano 33 BLE
 
 - Try increasing and decreasing the number of recordings per gesture, how does this impact performance?
 
@@ -19,6 +19,7 @@ Now that you have things working, here are a few new things to try.
 
 - Check out the examples in the [TensorFlow Lite Getting Started Guide](https://www.tensorflow.org/lite/microcontrollers/get_started)
 
+<!--
 - Try the [Arudino Fruit to Emoji](https://github.com/arduino/ArduinoTensorFlowLiteTutorials/tree/master/FruitToEmoji) Tutorial
-
+-->
 - Grab the [TinyML book](http://shop.oreilly.com/product/0636920254508.do) to continue at home

exercises/exercise10.md

@@ -1,10 +1,10 @@
 # Exercise 2: Source Code
 
-The [source code](https://github.com/arduino/AIoT-Dev-Summit-2019) for this workshop is available on Github in the Arduino organizaton.
+The source code and documentation for this workshop available on Github. The code is in the [ArduinoSketches](../ArduinoSketches) folder.
 
 If you're familiar with git and Github, clone the repository to your computer. If that last sentence didn't make sense to you,  we recommend that you download the code.
 
-1. Go to https://github.com/arduino/AIoT-Dev-Summit-2019
+1. Go to https://github.com/don/tinyml-workshop
 1. Click the green **Clone or download** button
 1. Choose Download ZIP
 1. Go to your downloads folder and extract all the files from the zip archive

exercises/exercise2.md

@@ -1,10 +1,10 @@
 # Exercise 3: Hardware
 
-![Picture of Arduino Nano 33 BLE Sense board](images/ble-sense.jpg)
+![Picture of Arduino Nano 33 BLE board](images/nano-33-ble.jpg)
 
 ## Unboxing and set up
 
-1. Remove the Arduino Nano 33 BLE Sense board from the box
+1. Remove the Arduino Nano 33 BLE board from the box
 1. Plug the micro USB cable into the board and your computer
 1. Open the Arduino IDE application on your computer
 1. Choose the board `Tools -> Board -> Arduino Nano 33 BLE`
@@ -21,7 +21,7 @@
 1. Open the serial monitor `Tools -> Serial Monitor` to see debug messages
 1. If everything is working properly, the LED will start blinking quickly
 
-__NOTE:__ The first time you upload a sketch to the Arduino Nano 33 BLE Sense, the USB port name might change. If this happens, you need to re-select the port using the `Tools -> Port` menu.
+__NOTE:__ The first time you upload a sketch to the Arduino Nano 33 BLE, the USB port name might change. If this happens, you need to re-select the port using the `Tools -> Port` menu.
 
 Next [Exercise 4: Visualize the IMU Data](exercise4.md)
 

exercises/exercise3.md

@@ -2,7 +2,7 @@
 
 The next step is to use an Arduino program you downloaded in [Exercise 2](exercise2.md) to capture motion data from the IMU.
 
-1. Open __AIoT-Dev-Summit-2019/ArduinoSketches/IMU_Capture/IMU_Capture.ino__ in the Arduino IDE.
+1. Open __tinyml-workshop/ArduinoSketches/IMU_Capture/IMU_Capture.ino__ in the Arduino IDE.
 1. Compile the sketch and upload it to the board: `Sketch -> Upload`
 1. Open the Serial Monitor: `Tools -> Serial Monitor`
 1. Shake the board. The change in acceleration will start recording data from the IMU in the Serial Monitor

exercises/exercise4.md

@@ -18,7 +18,7 @@ You can enable 3rd party cookies, or better yet, add an exception for `[*.]googl
 
 Open the [arduino_tinyml_workshop.ipynb](../arduino_tinyml_workshop.ipynb) notebook in Google Colab and follow the instructions in the notebook to prepare the data and train the model.
 
-https://colab.research.google.com/github/arduino/AIoT-Dev-Summit-2019/blob/master/arduino_tinyml_workshop.ipynb
+https://colab.research.google.com/github/don/tinyml-workshop/blob/master/arduino_tinyml_workshop.ipynb
 
 
 Next [Exercise 7: Classifying IMU Data](exercise7.md)

exercises/exercise6.md

@@ -1,6 +1,6 @@
 # Exercise 7: Classifying IMU Data
 
-1. Open __AIoT-Dev-Summit-2019/ArduinoSketches/IMU_Classifier/IMU_Classifier.ino__ in the Arduino IDE.
+1. Open __tinyml-workshop/ArduinoSketches/IMU_Classifier/IMU_Classifier.ino__ in the Arduino IDE.
 1. Switch to the model.h tab
 1. Replace the contents of model.h with the version you downloaded from Colab
 1. Upload the sketch: `Sketch -> Upload`

exercises/exercise7.md

@@ -4,7 +4,7 @@
 
 Now that we the code can recognize gestures, let's try printing out some emojis in addition to the text.
 
-Open __AIoT-Dev-Summit-2019/ArduinoSketches/IMU_Classifier/IMU_Classifier.ino__ in the Arduino IDE if it's not already loaded.
+Open __tinyml-workshop/ArduinoSketches/IMU_Classifier/IMU_Classifier.ino__ in the Arduino IDE if it's not already loaded.
 
 Create a new char* array named `EMOJIS`. Inside the array defines the emojis as a UTF-8 encoded unicode strings. This array must be the same length at the `GESTURE` array. The order of the emojis in the array must match the order of the gestures.
 

exercises/exercise8.md

@@ -33,6 +33,6 @@ Now you are ready to build a keyboard. Create a new sketch in the Arduino IDE. C
 
 ### Note
 
-__NOTE:__ once you load code that runs the USB Keyboard, the Arduino IDE might not be able to see the serial port when you want to load new code. Double click the reset button on the Nano 33 BLE Sense before you run __Sketch -> Upload__.
+__NOTE:__ once you load code that runs the USB Keyboard, the Arduino IDE might not be able to see the serial port when you want to load new code. Double click the reset button on the Nano 33 BLE before you run __Sketch -> Upload__.
 
 Next [Exercise 10: Next Steps](exercise10.md)

exercises/exercise9.md

@@ -80,7 +80,7 @@ What you know -> | Supervised Learning | -> What you want to know
 
 Running ML models on microcontrollers. Low power, below 1 mW.
 
-## Arduino Nano 33 BLE Sense board
+## Arduino Nano 33 BLE board
 
 Based on the Nordic nRF52840
 * Arm Cortex-M4F running at 64 MHz
@@ -90,8 +90,8 @@ Based on the Nordic nRF52840
 
 Onboard Sensors
 * IMU (measure motion: acceleration, gyro)
-* Temperature, pressure, humidity, light, color
-* PDM microphone
+
+This workshop also works on the Arduino Nano 33 BLE Sense board. The Sense version of the board adds additional sensors: temperature, pressure, humidity, light, color, PDM microphone.
 
 ## What is an IMU?
 
@@ -105,7 +105,6 @@ This board uses the ST Micro LSM9DS1.
 
 We're only using the accelerometer and gyroscope for this project.
 
-
 ## Workshop
 
 * Record IMU data
@@ -124,7 +123,6 @@ We're only using the accelerometer and gyroscope for this project.
 
 ## IMU input data
 
-
 The Arduino library enables the sensor to report 119 data points every second, this means a new set of data is received every 8.4ms