![\"\"](\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2022\/03\/tinyML-Starter-Kit-1030x773.png\")
<\/a><\/figure>\n\n\n\ntinyML\u00ae Starter Kit – Course in a Box: Getting Started with Ultra-Low Power Machine Learning at the Edge<\/span><\/h2>\n\n\n\nSince 2021, there has been continuous communication between Professor Vijay Janapa Reddi, tinyML Foundation, Edge Impulse, and Seeed. Seeed is honored to join hands with these stakeholders on launching the tinyML\u00ae Starter Kit – Course in a Box. With more than three months of testing, refining and optimization, we are excited to release it. The kit consists of curriculum & resources and hardware kits.<\/p>\n\n\n\n
Curriculum & Resources: Course in a Box<\/span><\/h2>\n\n\n\nDeveloped with the concept of Course in a Box, the tinyML\u00ae Starter Kit Curriculum, customized by Professor Vijay Janapa Reddi together with Seeed Edu Team, is explicitly designed for both educators and learners to embrace the emerging tiny Machine Learning. The full curriculum is accessible by scanning the QR code on the Kit’s Packaging.<\/p>\n\n\n\n![\"\"](\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2022\/03\/\u672a\u547d\u540d-773x1030.jpg\")
<\/a><\/figure>\n\n\n\nThis curriculum aims to serve as a hands-on booklet for educators to adopt the Wio Terminal into the classroom or workshops to show learners the power of ultra-low-power Machine Learning at the edge. It provides the basic underpinnings that one would have to cover to teach the very basics of ML while keeping the concepts grounded in hands-on exercises.<\/p>\n\n\n\n
Users of this curriculum will learn how to train and deploy deep neural network models on Seeed Wio Terminal. Course content features five detailed step-by-step projects that will allow students to grasp basic ideas about modern Machine Learning and how you can use it in low-power and footprint microcontrollers to create intelligent and connected systems.<\/p>\n\n\n\n
After completing the course, you will be able to design and implement their Machine Learning enabled projects using the tinyML\u00ae Starter Kit, starting from defining a problem to gathering data and training the neural network model and finally deploying it to the device to display inference results or control other hardware appliances based on inference data. Course content is based on using the Edge Impulse platform, which simplifies the data collection\/ model training\/ conversion pipeline.<\/p>\n\n\n\n![\"\"](\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2022\/03\/Quote-by-Vijay-Janapa-Reddi-1030x961.png\")
<\/a><\/figure>\n\n\n\nThe Hardware of tinyML\u00ae Starter Kit<\/span><\/h2>\n\n\n\nThe Kit includes a Wio Terminal<\/a>, a Grove Ultrasonic Sensor<\/a>, and a Grove Temperature & Humidity Sensor<\/a> (Click here to have more details<\/a>). Wio Terminal is a fully open-source device, compatible with Arduino and Micropython, built with an ATSAMD51 microcontroller with wireless connectivity supported by Realtek RTL8720DN. Wio Terminal is highly-integrated with a 2.4\u201d LCD Screen, and there is an onboard IMU, microphone, buzzer, microSD card slot, light sensor, and an infrared emitter, that is all safely nestled in a compact enclosure. Thanks to a high-performing CPU, Bluetooth, and Wi-Fi powered by Realtek RTL8720DN chip, Wio Terminal is a great fit to run ultra-low power Machine Learning algorithms for various AIoT education, projects, prototyping, and solutions. Moreover, since it is certified by Microsoft Azure, Wio Terminal can be connected to Azure IoT Central, which frees limited innovation and creation possibilities. Together with 2 Grove sensor modules in the kit, people with no tech background can enjoy the plug-and-play project building hands-on experience to learn about ultra-low power Machine Learning at the edge and how to collect data to train and deploy models for specific applications.<\/p>\n\n\n\n![\"\"](\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2022\/03\/tinyML-Starter-Kit-2-1.png\")
<\/a><\/figure>\n\n\n\nWith the hardware kit, student experiences, and curated curriculum, you can get hands-on experience, learn about the full circle of Machine Learning algorithms (Data Collection, Pre-processing, Feature Extraction, Model Training, Model Optimizations, ML Model Deployment) whether you are in the classroom, at home, or through distance learning courses, and then you can apply the knowledge to build tinyML projects in the real world.<\/p>\n\n\n\n
We are honored and pleased to join hands with Professor Vijay Janapa Reddi, tinyML Foundation, Edge Impulse to make the technology and resources available for both educators and learners, enabling them the knowledge and skills to master edge ML and to start their journey of tiny Machine Learning. We are eager to see more organizations, institutions, companies, and individuals, especially the next generation joining this endeavor, and step by step, we can march further to a sustainable world!<\/p>\n\n\n\n
<\/p>\n\n\n\n
Acknowledgment Note<\/strong>: tinyML\u00ae is a registered trademark of tinyML Foundation<\/a> and is used by permission.<\/p>\n\n\n\n
\n\n\n\nAbout Professor Vijay Janapa Reddi<\/strong>
Vijay Janapa Reddi is an Associate Professor at Harvard University, Inference Co-chair for MLPerf, and a founding member of MLCommons, a nonprofit ML (Machine Learning) organization aiming to accelerate ML innovation. He also serves on the MLCommons board of directors. Before joining Harvard, he was an Associate Professor at The University of Texas at Austin in the Department of Electrical and Computer Engineering. His research interests include computer architecture and runtime systems, specifically in the context of autonomous machines and mobile and edge computing systems. He received a Ph.D. in computer science from Harvard University, M.S. from the University of Colorado at Boulder and B.S from Santa Clara University. https:\/\/scholar.harvard.edu\/vijay-janapa-reddi\/bio<\/a><\/p>\n\n\n\n<\/p>\n\n\n\n
About tinyML Foundation<\/strong>
tinyML Foundation is a non-profit professional organization focused on supporting and nurturing the fast-growing branch of ultra-low power machine learning technologies and approaches dealing with machine intelligence at the very edge of the cloud. Seeed is happy and honored to be a Gold Strategic Partner of tinyML Foundation as our way to contribute to serving the emerging community. https:\/\/www.tinyml.org\/<\/a><\/p>\n\n\n\n<\/p>\n\n\n\n
Developed with the concept of Course in a Box, the tinyML\u00ae Starter Kit Curriculum, customized by Professor Vijay Janapa Reddi together with Seeed Edu Team, is explicitly designed for both educators and learners to embrace the emerging tiny Machine Learning. The full curriculum is accessible by scanning the QR code on the Kit’s Packaging.<\/p>\n\n\n\n This curriculum aims to serve as a hands-on booklet for educators to adopt the Wio Terminal into the classroom or workshops to show learners the power of ultra-low-power Machine Learning at the edge. It provides the basic underpinnings that one would have to cover to teach the very basics of ML while keeping the concepts grounded in hands-on exercises.<\/p>\n\n\n\n Users of this curriculum will learn how to train and deploy deep neural network models on Seeed Wio Terminal. Course content features five detailed step-by-step projects that will allow students to grasp basic ideas about modern Machine Learning and how you can use it in low-power and footprint microcontrollers to create intelligent and connected systems.<\/p>\n\n\n\n After completing the course, you will be able to design and implement their Machine Learning enabled projects using the tinyML\u00ae Starter Kit, starting from defining a problem to gathering data and training the neural network model and finally deploying it to the device to display inference results or control other hardware appliances based on inference data. Course content is based on using the Edge Impulse platform, which simplifies the data collection\/ model training\/ conversion pipeline.<\/p>\n\n\n\n The Kit includes a Wio Terminal<\/a>, a Grove Ultrasonic Sensor<\/a>, and a Grove Temperature & Humidity Sensor<\/a> (Click here to have more details<\/a>). Wio Terminal is a fully open-source device, compatible with Arduino and Micropython, built with an ATSAMD51 microcontroller with wireless connectivity supported by Realtek RTL8720DN. Wio Terminal is highly-integrated with a 2.4\u201d LCD Screen, and there is an onboard IMU, microphone, buzzer, microSD card slot, light sensor, and an infrared emitter, that is all safely nestled in a compact enclosure. Thanks to a high-performing CPU, Bluetooth, and Wi-Fi powered by Realtek RTL8720DN chip, Wio Terminal is a great fit to run ultra-low power Machine Learning algorithms for various AIoT education, projects, prototyping, and solutions. Moreover, since it is certified by Microsoft Azure, Wio Terminal can be connected to Azure IoT Central, which frees limited innovation and creation possibilities. Together with 2 Grove sensor modules in the kit, people with no tech background can enjoy the plug-and-play project building hands-on experience to learn about ultra-low power Machine Learning at the edge and how to collect data to train and deploy models for specific applications.<\/p>\n\n\n\n With the hardware kit, student experiences, and curated curriculum, you can get hands-on experience, learn about the full circle of Machine Learning algorithms (Data Collection, Pre-processing, Feature Extraction, Model Training, Model Optimizations, ML Model Deployment) whether you are in the classroom, at home, or through distance learning courses, and then you can apply the knowledge to build tinyML projects in the real world.<\/p>\n\n\n\n We are honored and pleased to join hands with Professor Vijay Janapa Reddi, tinyML Foundation, Edge Impulse to make the technology and resources available for both educators and learners, enabling them the knowledge and skills to master edge ML and to start their journey of tiny Machine Learning. We are eager to see more organizations, institutions, companies, and individuals, especially the next generation joining this endeavor, and step by step, we can march further to a sustainable world!<\/p>\n\n\n\n <\/p>\n\n\n\n Acknowledgment Note<\/strong>: tinyML\u00ae is a registered trademark of tinyML Foundation<\/a> and is used by permission.<\/p>\n\n\n\n About Professor Vijay Janapa Reddi<\/strong> <\/p>\n\n\n\n About tinyML Foundation<\/strong> <\/p>\n\n\n\n<\/a><\/figure>\n\n\n\n<\/a><\/figure>\n\n\n\nThe Hardware of tinyML\u00ae Starter Kit<\/span><\/h2>\n\n\n\n
<\/a><\/figure>\n\n\n\n
\n\n\n\n
Vijay Janapa Reddi is an Associate Professor at Harvard University, Inference Co-chair for MLPerf, and a founding member of MLCommons, a nonprofit ML (Machine Learning) organization aiming to accelerate ML innovation. He also serves on the MLCommons board of directors. Before joining Harvard, he was an Associate Professor at The University of Texas at Austin in the Department of Electrical and Computer Engineering. His research interests include computer architecture and runtime systems, specifically in the context of autonomous machines and mobile and edge computing systems. He received a Ph.D. in computer science from Harvard University, M.S. from the University of Colorado at Boulder and B.S from Santa Clara University. https:\/\/scholar.harvard.edu\/vijay-janapa-reddi\/bio<\/a><\/p>\n\n\n\n
tinyML Foundation is a non-profit professional organization focused on supporting and nurturing the fast-growing branch of ultra-low power machine learning technologies and approaches dealing with machine intelligence at the very edge of the cloud. Seeed is happy and honored to be a Gold Strategic Partner of tinyML Foundation as our way to contribute to serving the emerging community. https:\/\/www.tinyml.org\/<\/a><\/p>\n\n\n\n