Train on Microcontroller Devices you own this product

intermediate Python • basic machine learning • basic edge computing systems • intermediate TensorFlow
skills learned
post-training quantization using tf-dataset • quantization awareness training and JIT • batch normalization for quantized model
Kanishka Tyagi & Raghavendra Sriram
1 week · 6-8 hours per week · INTERMEDIATE

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EKKo Inc., a machine learning consultancy, is working on an embedded system to enable deaf or hard of hearing people to participate in online meetings and events on their mobile devices. Your task as a data scientist is to optimize this system. Using the model optimization toolkit for TensorFlow, you’ll train a CNN model with an existing American Sign Language (ASL) dataset and convert it to TFLite format to reduce the code footprint. You’ll optimize it further using quantization, drastically reducing size and CPU consumption while maintaining model accuracy. Using batch normalization to decrease the number of training cycles, you’ll significantly speed up the CNN model’s training process. Lastly, you’ll integrate the quantization changes you’ve made by compiling and training the CNN model. When you’re finished, you’ll have a fully functional quantized CNN that can be run successfully on an embedded device.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project authors

Kanishka Tyagi

Dr. Kanishka Tyagi received his bachelor's degree in electrical engineering in 2008 from Pantnagar, India. Later he worked as a research associate at the Department of Electrical Engineering, Indian Institute of Technology, Kanpur, with Dr. P.K.Kalra. He received his master’s and doctoral degree with Dr. Michael Manry in the Department of Electrical Engineering at The University of Texas at Arlington in 2012 and 2017. Currently, he works as a senior machine learning scientist at Aptiv advance research center, California. Prior to Aptiv, he worked at Siemens research, and interned in machine learning groups at The MathWorks and Google Research. He has worked as a visiting researcher at Ajou University and Seoul National University. He received the 2007 and 2011 IEEE CIS Outstanding Student Paper Travel Grant Award and 2013 IEEE CIS Walter Karplus Summer Research Grant award. Dr. Tyagi is an IEEE senior member and member of various IEEE-CIS committees. He currently serves as an associate editor for IEEE Transaction on Neural Network and Learning Systems. Dr. Tyagi has published over 30 papers and filed 17 U.S. patents and trade secrets.

Raghavendra Sriram

Raghavendra Sriram completed his bachelor’s degree in engineering at the Department of Electrical and Electronics Engineering at Canara Engineering College in Mangalore in 2012. Currently, he’s a senior development engineer for OBD systems working at Paccar, Inc. in Mt. Vernon, WA. His work focuses on developing innovative solutions to diagnostic capabilities for diesel engine misfire detection and calibration efforts using machine learning and optimization techniques. Previously, he developed and designed diagnostic algorithms for after-treatment systems. Before joining the industry, he worked as a researcher under the guidance of Dr. Frank L. Lewis in the electrical department at the University of Texas at Arlington, focusing mainly on developing and implementing intelligent control algorithms on various robotics platforms. He has extensive experience with multiple rapid prototyping systems and tools, has been awarded several university department scholarships for research, and contributed to several journals and research groups.


The liveProject is for intermediate Python programmers who know the basics of data science. To begin these liveProjects you’ll need to be familiar with the following:

  • Raspberry Pi/edge computer
  • Ubuntu Desktop 18.04 +
  • Intermediate Python
  • Basic Jupyter Notebook
  • Intermediate NumPy
  • Intermediate TensorFlow
  • Conda/pip virtual environment setup skills
  • Basic data science
  • Understand logistic regressions and classification
  • Load data on a neural network
  • Train a neural network
  • Assess a CNN model
  • Basics of linear algebra (vectors, spaces, matrix transformations)


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