https://github.com/machine-learning-tokyo/edgeai-lab-microcontroller-series

This repository is to share the EdgeAI Lab with Microcontrollers Series material to the entire community. We will share documents, presentations and source code of two demo applications.
https://github.com/machine-learning-tokyo/edgeai-lab-microcontroller-series

arduino edgeai hardware machinelearning

Last synced: about 2 months ago
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This repository is to share the EdgeAI Lab with Microcontrollers Series material to the entire community. We will share documents, presentations and source code of two demo applications.

• Host: GitHub
• URL: https://github.com/machine-learning-tokyo/edgeai-lab-microcontroller-series
• Owner: Machine-Learning-Tokyo
• License: apache-2.0
• Created: 2021-07-29T11:41:32.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2021-10-14T09:40:46.000Z (about 3 years ago)
• Last Synced: 2023-03-03T12:56:48.784Z (almost 2 years ago)
• Topics: arduino, edgeai, hardware, machinelearning
• Language: C++
• Homepage:
• Size: 26.1 MB
• Stars: 15
• Watchers: 5
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Introduction

**MLT Edge AI Lab** was formed in 2020 when a team of researchers/engineers visited a local farm in Chiba to brainstorm solutions for problems faced by local farmers. EdgeAI Lab was formed with an aim of creating an OPEN environment where anyone can experiment with tools and apply their learnings to create quick prototypes.

**MLT Edge AI Lab with Microcontrollers Series** is created by **Naveen Kumar** and **Yoovraj Shinde**, to learn  and share about the steps in building EdgeAI applications. We will take a look at the general steps in edgeAI pipeline and the different hardwares available to deploy the models. We will also take a look at 2 simple applications, to strengthen our understanding about data collection/ processing/ building / deployment and inference.


We are looking forward to active participation from the MLT community members to build cool apps together. We will do a short brainstorming session in the end to find good ideas which we can work together throughout the series.

Our goal is to help the MLT Edge AI community to build their application by end of the series.

Join us on **#edge_ai_lab** channel on [MLT Slack](https://machinelearningtokyo.slack.com)

[Feedback Survey](https://forms.gle/811apJr1yesA9EdK9)

# Sessions

| Date | Topic | Description | Presentation Link | Video | 

| ---- | ----- | ----------- | ----------------- | ----- |

| 22 Aug 2021 | [Overview of EdgeAI Applications](session1/README.md) | Brief introduction of different hardware and short talk about the pipelines in edgeAI applications | [Slides](session1/slides.pdf) | [Video Recording](https://www.youtube.com/watch?v=S9Ejmi_3Vrw) |

| 29 Aug 2021 | [Motion Based Application using IMU](session2/README.md) | Walkthrough different blocks of pipeline for developing a motion based Edge AI Application. Type of data that can be extracted from IMU on Arduino Nano BLE Sense board. Whiteboard & Brainstorming | [Slides](session2/slides.pdf) | [Video Recording](https://www.youtube.com/watch?v=jIzV5BJcH6Y) |

| 05 Sep 2021 | [Audio Based Application using Microphone](session3/README.md) | Walkthrough different blocks of pipeline for developing a audio based Edge AI Application. Type of data that can be extracted from microphone on Arduino Nano BLE Sense board. Whiteboard & Brainstorming | [Slides](session3/slides.pdf) | [Video Recording](https://www.youtube.com/watch?v=Jxa_kI7ix5M) |

| 19 Sep 2021 | Wrap-up Session | Summary and team presentations | TBA | TBA |

# About Session Leads

[Naveen Kumar](https://www.hackster.io/naveenbskumar) is a Senior Technical Scientist at RIKEN working on microbial DNA sequencing data analysis. He is a maker, tinkerer, embedded electronics hobbyist, and Edge AI enthusiast. In his free time, he enjoys watching movies, photography, and playing with microcontrollers.

[Yoovraj Shinde](https://www.linkedin.com/in/yoovraj-shinde/) is Engineering Team Manager in the Research Department for Rakuten Institute of Technology, Tokyo. His background is in electronics engineering and loves to tinker around with circuits / hardware / robots for kids. He is interested in working around Machine Learning and Hardware, and learning new things. 

# Code of Conduct

MLT promotes an inclusive environment that values integrity, openness, and respect. https://github.com/Machine-Learning-Tokyo/MLT_starterkit