HACKSTER.IO PRESENTS
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Throughout the next year, we'll host monthly activities for you to take your skills to the edge with the Edge Impulse platform. Train a board, create a dataset or perform a task - show off your skills for a chance to win prizes!
When deploying smart devices in certain environments your available space and power requirements may be limited. The Silicon Labs xG24 Developer Kit is a sensor-rich platform for prototyping AIoT devices, able to run TinyML models on just a coin-cell battery. With a dedicated AI accelerator, 6 onboard sensors, BLE, and a compact board about the size of two postage stamps, the xG24 can fit in tight places and capture a wealth of environmental information.
To better understand the Silicon Labs xG24 Developer Kit and it’s capabilities, we're highlighting two example resources that make great use of Edge Impulse running on the board. Review the resources below, then take a short quiz about what you learned to be entered for a chance at winning an xG24 in January's prize bundle!
Join the Edge Impulse community at hackster.io/EdgeImpulse.
Review the two resources below & complete the quiz.
Project: Occupancy Sensing - SiLabs EFR32MG24
Tutorial: Object detection - SiLabs xG24 Dev Kit
Quiz closes January 29, 2023 at 11:59PM PT.
Edge Impulse will be giving away three prize bundles to eligible activity submissions.
Winners will be announced February 6, 2023.
Edge Impulse Swag
Use the resources below to learn more about the Silicon Labs xG24 Dev Kit and how to get started collecting data, building and training tiny machine learning models, and deploying these models to the xG24 Dev Kit directly from the Edge Impulse Studio.
Documentation
Announcing Official Support for the Silicon Labs xG24 Dev Kit
Getting Started with SiLabs xG24 Dev Kit
Setting up your EFR32xG24 Kit
xG24 Dev Kit User's Guide
MG24 Demo
Learn more about Silicon Labs' cost-effective, feature-rich, prototype and development
platform based on the EFR32™ Wireless Gecko System-on-Chip in the official xG24 Dev Kit User's Guide.
Other Project Tutorials
Explore more projects making use of Silicon Labs hardware including the xG24’s very-capable predecessor, the Thunderboard Sense 2.
Take an existing Edge Impulse model built for the Thunderboard Sense 2, and prepare it for use on the SiLabs xG24 board.
Use Edge Impulse to train a model for detecting gestures with the onboard accelerometer.
Edge Impulse based TinyML model using Audio data acquisition to predict the vehicle failures like faulty drive shaft, Brake pad noises.
Find more projects featuring Edge Impulse on Hackster.io.
You must be registered on Edge Impulse to be eligible to win Take It to the Edge Activities.
Click on the links below for details, learning resources and winner announcements from our past activities and featured technologies.
Keyword spotting, otherwise known as wake word detection, is a form of voice recognition that allows computers to listen for and respond to specific words. In keyword spotting, a device listens for a keyword or phrase and triggers an action based on what is spoken. Review the resources below to gain a better understanding of this popular technology and learn how to train a device to recognize your own keyword or phrase.
The Raspberry Pi Pico is a low-cost development board that use a microcontroller (Cortex-M) processor. The Pico (and brand-new Pico W) are low-power, small devices that have more modest capabilities than a regular Raspberry Pi, but excel when it comes to size, battery life, and ease of prototyping. With 264kB on-chip SRAM and 2MB on-board flash, the Pico can even run lightweight tinyML models for basic sensor-driven AI workloads.
The Arduino Nano 33 BLE Sense is one of the most feature-packed Arduino boards, with a built-in temperature sensor, accelerometer, gyroscope, and microphone (and more). This makes it a great platform for building monitoring and sensing projects and devices. You'll be able to sample raw data, build models, and deploy trained machine learning models directly from the Edge Impulse Studio.
The ESP32 is a small, low cost microcontroller with bluetooth and WiFi connectivity, making it ideal for IoT products and applications. It's dual-core processor is strong enough to run TinyML models, opening up endless possibilities for building smart sensors, wearables / health devices, basic vision applications, and more.
The Espressif ESP-EYE development board based on Espressif's ESP32 chip is fully supported by Edge Impulse. The ESP-EYE is equipped with a 2-Megapixel camera and a microphone making it perfect for image recognition and audio processing applications.
In most cases, data is the most important factor in creating a successful TinyML project. Sure, hardware selection, power, and connectivity matter too, but without good training data, your model likely won’t be accurate enough to perform it’s intended function out in the world!
The team at Sony has created a small but powerful microcontroller board that is ready to run Edge Impulse machine learning models. With 6 Arm Cortex-M4 cores, 1.5 mb of RAM, GPS, camera input, onboard audio, and plenty of expansion options for sensors, WiFi, cellular and more, the Spresense can be used in a wide variety of projects.
Many times, we focus on performing machine learning locally on-device, with no need for internet connectivity. This reduces latency, simplifies projects, and can sometimes reduce costs. However, some projects do require connectivity to the cloud in order to set a status, update a dashboard, log an activity or event, or allow a user to interact with a device.
Everyone is eligible to complete activities and learn about Edge Impulse. However, you are only eligible to win or be awarded prizes if:
Winners will be announced on this page within 5 business days after a quiz is closed. Winners will also be contacted via the email used when submitting their quiz.
The prize fulfillment process will take up to 8 weeks after winners have been announced. You will receive an email when your prize is shipped. If you need to make any changes to your address during that time, please contact us at events@hackster.io.
No, Hackster will ship all the prizes and will cover the taxes/customs fees associated with shipment.
If you have any questions regarding this giveaway, please contact us at events@hackster.io.
Find upcoming events, workshops, and more!
Hackster, an Avnet Community © 2022
Keyword spotting, otherwise known as wake word detection, is a form of voice recognition that allows computers to listen for and respond to specific words. In keyword spotting, a device listens for a keyword or phrase and triggers an action based on what is spoken. There are many applications for keyword spotting including home automation, customer support services and more. Review the resources below to gain a better understanding of this popular technology and learn how to train a device to recognize your own keyword or phrase.
For our first challenge, we'd like you to teach a device to recognize the phrase, “Take it to the Edge”! We have a simple guided walkthrough at studio.edgeimpulse.com/evaluate that will help you along the way.
Follow the steps below to complete this activity and be entered into our giveaway to win a Syntiant TinyML board and Edge Impulse swag. This activity can be completed using a pc, mobile phone or any Edge Impulse supported device.
Video: Build Your Own ML-Powered Keyword Spotting Model in 30KB RAM
Learn the steps required to build a real TinyML model that responds to your voice. No pretrained models, no already created datasets, and no fixed keywords. You'll learn how to collect data from one of our fully supported development boards or your mobile phone, how to train an ML model, and how to deploy this back to your device where the model classifies audio in realtime.
Prefer to read through a tutorial rather than watch a video? Check out the written tutorial version of this demo "Responding to your voice" at the link below.
Get started with Edge Impulse by reading through the docs. In the Edge Impulse docs you'll find user guides, tutorials and API documentation.
Use machine learning to build a system that can recognize when a particular sound is happening—a task known as audio classification.
Learn how you can add turn signals on your bicycle and use Edge Impulse with an Arduino Nano BLE to warn others which direction you are going by using "left" and "right" keywords.
Edge Impulse Swag
Terms and Conditions
Official Terms and Conditions
These are the Official Terms and Conditions (“Terms”) that govern the relationship between Hackster.io, an Avnet community ("Administrator"), Edge Impulse ("Sponsor") and you (the “Entrant”) in relation to this developer program (“Take It to the Edge”). No purchase necessary to enter or win. Void where prohibited. A purchase of any kind does not increase your chance of winning.
TAKE IT TO THE EDGE DETAILS:
Take It to the Edge will start at 9AM Pacific Time (“PT”) on June 16, 2022 and end at 11:59 PT on March 26, 2023 (the “Giveaway Period”), subject to entry periods as outlined in the “ENTRY PERIODS” section below.
ENTRY PERIODS:
Activity Entry Period Dates:
Activity 1: June 16, 2022 - July 3, 2022
Activity 2: July 11, 2022 - July 24, 2022
Activity 3: August 15, 2022 - August 28, 2022
Activity 4: September 12, 2022 - September 25, 2022
Activity 5: October 10, 2022 - October 23, 2022
Activity 6: November 14, 2022 - November 27, 2022
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Activity 1: Week of July 4, 2022
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Activity 8: Week of January 30, 2023
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Activity 10: Week of March 27, 2023
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There are many applications for keyword spotting including home automation, customer support services and more. Review the resources below to gain a better understanding of this popular technology and learn how to train a device to recognize your own keyword or phrase.
Teach a device to recognize the phrase, “Take it to the Edge”! We have a simple guided walkthrough at studio.edgeimpulse.com/evaluate that will help you along the way.
Follow the steps below to complete this activity and be entered into our giveaway to win a Syntiant TinyML board and Edge Impulse swag. This activity can be completed using a pc, mobile phone or any Edge Impulse supported device.
Video: Build Your Own ML-Powered Keyword Spotting Model in 30KB RAM
Learn the steps required to build a real TinyML model that responds to your voice. No pretrained models, no already created datasets, and no fixed keywords. You'll learn how to collect data from one of our fully supported development boards or your mobile phone, how to train an ML model, and how to deploy this back to your device where the model classifies audio in realtime.
Prefer to read through a tutorial rather than watch a video? Check out the written tutorial version of this demo "Responding to your voice" at the link below.
Get started with Edge Impulse by reading through the docs. In the Edge Impulse docs you'll find user guides, tutorials and API documentation.
Use machine learning to build a system that can recognize when a particular sound is happening—a task known as audio classification.
Learn how you can add turn signals on your bicycle and use Edge Impulse with an Arduino Nano BLE to warn others which direction you are going by using "left" and "right" keywords.
Edge Impulse Swag
Use the resources below to learn about the Raspberry Pi Pico, and how when combined with Edge Impulse, it can make an impact across a wide variety of topics like sustainability and environment, health, industry, energy, and more.
For July's challenge, read the two projects below to learn about various applications that can be built with the Pico or Pico W and Edge Impulse, then take a short quiz to show us what you learned! Quiz participants will be entered in the drawing to win July's prize bundle - a Raspberry Pi Pico, Grove Starter Kit for the Pico and Edge Impulse Swag.
Documentation
Learn how to connect Raspberry Pi's RP2040 to Edge Impulse, by flashing the latest firmware and using the Edge Impulse CLI to configure the device.
Project Tutorials
Pen + pineapple = Pineapplepen. What about Pico + Machine Learning?
An intelligent device for seniors which detects falls and sends emergency alert messages with location information using a cellular network.
Using machine learning to build a gesture recognition system that runs on a tiny microcontroller, the RP2040.
Raspberry Pi Pico runs Edge Impulse’s Sensor Fusion software to read data from gas, temperature, and humidity sensors and tells you if your food is burning.
Find more projects featuring Edge Impulse and Raspberry Pi Pico on Hackster.io.
Raspberry Pi Pico
Seeed Studio Grove Starter Kit for the Pico
Edge Impulse Swag
Use the resources below to learn about the Raspberry Pi Pico, and how when combined with Edge Impulse, it can make an impact across a wide variety of topics like sustainability and environment, health, industry, energy, and more.
For July's challenge, read the two projects below to learn about various applications that can be built with the Pico or Pico W and Edge Impulse, then take a short quiz to show us what you learned! Quiz participants will be entered in the drawing to win July's prize bundle - a Raspberry Pi Pico, Grove Starter Kit for the Pico and Edge Impulse Swag.
Documentation
Learn how to connect Raspberry Pi's RP2040 to Edge Impulse, by flashing the latest firmware and using the Edge Impulse CLI to configure the device.
Project Tutorials
Pen + pineapple = Pineapplepen. What about Pico + Machine Learning?
An intelligent device for seniors which detects falls and sends emergency alert messages with location information using a cellular network.
Using machine learning to build a gesture recognition system that runs on a tiny microcontroller, the RP2040.
Raspberry Pi Pico runs Edge Impulse’s Sensor Fusion software to read data from gas, temperature, and humidity sensors and tells you if your food is burning.
Find more projects featuring Edge Impulse and Raspberry Pi Pico on Hackster.io.
Raspberry Pi Pico
Seeed Studio Grove Starter Kit for the Pico
Edge Impulse Swag
The Arduino Nano 33 BLE Sense is one of the most feature-packed Arduino boards, with a built-in temperature sensor, accelerometer, gyroscope, and microphone (and more). This makes it a great platform for building monitoring and sensing projects and devices. You'll be able to sample raw data, build models, and deploy trained machine learning models directly from the Edge Impulse Studio.
We've chosen two Hackster projects that show off the power of Edge Impulse on Arduino. Read the projects below to learn about voice control and digit recognition applications using the Arduino Nano 33 BLE Sense and Edge Impulse, then take a short quiz to show us what you learned and for a chance to win August's prize bundle.
Use the resources below to learn about the Arduino Nano 33 BLE Sense, and how you can use it with Edge Impulse to build a wide variety of projects covering sustainability and the environment, health, industry, energy, and more.
Documentation
how to connect an Arduino Nano 33 BLE Sense to Edge Impulse, by flashing the latest firmware and using the Edge Impulse CLI to configure the device.
Other Project Tutorials
BABL leverages tinyML to distinguish a baby's cry from other noise, preventing false alarms, and alerting parents only when needed.
TinyML implementation to identify running water tap sound and once heard one, a buzzer + LED timer is triggered.
A fun and simple project that uses tinyML to detect and respond to dog barks.
How do you tell the difference between a fall and a sudden movement? Train a machine learning model to detect when a fall occurs.
Find more projects featuring Edge Impulse and the Arduino Nano 33 BLE Sense on Hackster.io.
Edge Impulse Swag
The ESP32 is a small, low cost microcontroller with bluetooth and WiFi connectivity, making it ideal for IoT products and applications. It's dual-core processor is strong enough to run TinyML models, opening up endless possibilities for building smart sensors, wearables / health devices, basic vision applications, and more.
The Espressif ESP-EYE development board based on Espressif's ESP32 chip is fully supported by Edge Impulse. The ESP-EYE is equipped with a 2-Megapixel camera and a microphone making it perfect for image recognition and audio processing applications.
Learn More >>
Follow the steps below to get acquainted with Edge Impulse and the ESP32’s capabilities using the Edge ML platform. Then take a short quiz for a chance to win September's prize bundle!
Join the Edge Impulse community at hackster.io/EdgeImpulse.
Sign up for an Edge Impulse account at studio.edgeimpulse.com/signup.
Review the projects below & complete the quiz.
Gesture Classification with ESP32 and TinyML
ESP32-CAM: TinyML Image Classification - Fruits vs Veggies
Documentation
Announcing Official Support for Espressif ESP-EYE (ESP32)
Getting Started with Edge Impulse on Espressif ESP-EYE (ESP32)
Add Sight to Your ESP32
Learn how to flash the latest firmware and use the Edge Impulse CLI to configure the ESP32.
More Project Tutorials
Edge Driving Monitor
Driving Monitor that tracks the driver's blind spots with a warning system created with an ESP32 CAM and powered by Edge Impulse.
Use an ESP-32 Cam and an Edge Impulse model to take photos of your avian friends visiting your solar-powered bird feeder!
Find more projects featuring Edge Impulse and ESP32 on Hackster.io
Edge Impulse Swag
In most cases, data is the most important factor in creating a successful TinyML project. Sure, hardware selection, power, and connectivity matter too, but without good training data, your model likely won’t be accurate enough to perform it’s intended function out in the world!
Understanding the significance of data in TinyML models is extremely important. Get acquainted with the critical role data plays in an Edge Impulse project by reading through the two official Edge Impulse Docs below. Learn how to upload, label, augment, and split up your data, then take a short quiz for a chance to win an Arduino Portenta H7 + Vision Shield in October’s prize bundle.
Join the Edge Impulse community at hackster.io/EdgeImpulse.
Sign up for an Edge Impulse account at studio.edgeimpulse.com/signup.
Review the documents below & complete the quiz.
See how data is split for train/test set & the data distribution for each class in your dataset.
Explore your dataset, find outliers or mislabeled data, and label unlabeled data.
Documentation
Create Active Learning Pipelines with Data Sources and Data Explorer Features
Better Organize Your ML data with New Filters and Batch Operations
Data Augmentation
Project Tutorials Featuring Edge Impulse with the Arduino Portenta H7
A TinyML model using Arduino Portenta and Edge Impulse to predict the anomalous operation in Industrial machineries like Pump, valves & fans.
A low power vision-based sewer faults detection device using a tinyML neural network to produce highly accurate sewer faults classification.
As the name suggests, this device classifies the cloud into six different categories using TinyML.
Find more projects using Edge Impulse on Hackster.io
Edge Impulse Swag
The team at Sony has created a small but powerful microcontroller board that is ready to run Edge Impulse machine learning models. With 6 Arm Cortex-M4 cores, 1.5 mb of RAM, GPS, camera input, onboard audio, and plenty of expansion options for sensors, WiFi, cellular and more, the Spresense can be used in a wide variety of projects.
This is your chance to get up to speed on the Sony Spresense and it’s TinyML capabilities. Review two projects that make great use of Edge Impulse running on the Spresense and the getting started guide below, then take a short quiz for a shot at winning a Spresense of your own as part of November's prize bundle!
Join the Edge Impulse community at hackster.io/EdgeImpulse.
Sign up for an Edge Impulse account at studio.edgeimpulse.com/signup.
Review the three resources below & complete the quiz.
Free Car Parking Monitoring Device
Remote Leak Monitoring with AI Detection and Alerting
Sony's Spresense Getting Started Guide
Quiz closes November 27, 2022 at 11:59PM PT.
Documentation
Other Project Tutorials
Using AI and IoT to Help Grow Food in Remote Greenhouses
Use embedded ML on low-power microcontrollers to analyze crops in greenhouses without power & send the results over LPWAN networks
Using Sony Spresence & EI, turns analog oil tank meter into digital, realtime update on the app help delivery company optimize operation.
Using computer vision on a Sony Spresense to count traffic in a bicycle lane.
Estimating plant growth parameters for high throughput phenotyping using regression and EdgeImpulse on low power Sony Spresense under 0.35A!
Find more projects featuring Edge Impulse and Sony Spresense on Hackster.io.
Edge Impulse Swag
Many times, we focus on performing machine learning locally on-device, with no need for internet connectivity. This reduces latency, simplifies projects, and can sometimes reduce costs. However, some projects do require connectivity to the cloud in order to set a status, update a dashboard, log an activity or event, or allow a user to interact with a device.
We're highlighting two example projects that use Edge Impulse plus Blues Wireless to send data to the cloud. Review the projects below to better understand how you can leverage connectivity in your own projects, then take a short quiz about what you learned to be entered for a chance at winning a Blues Wireless Starter Kit as part of December's prize bundle!
Join the Edge Impulse community at hackster.io/EdgeImpulse.
Sign up for an Edge Impulse account at studio.edgeimpulse.com/signup.
Review the three resources below & complete the quiz.
Appliances Identifier Smart Energy Meter
Running Faucet Alert System with Blues Wireless
Quiz closes January 1, 2023 at 11:59PM PT.
Documentation
Sending Inference Data to the Cloud with the Notecard
Getting Started with Edge Impulse on Blues Wireless Swan
Edge Impulse Review of Audio Classification
Routing Data to Cloud Tutorials
Blues wireless offers Routing Data to Cloud tutorials that walk you through routing Notecard data to a variety of big clouds and IoT platforms.
Other Project Tutorials
Learn how to add the IoT and TinyML to analog sensors with the Raspberry Pi, Edge Impulse, and the Notecard.
Learn how to create a prototype of a roadside litter detection device that maps trash in cities using Blues Wireless and Edge Impulse.
Will guide you to build a cellular based predictive maintenance monitoring device with Blues Notecard, Edge Impulse, Qubitro & Wio Terminal.
Build an anomaly detection ML model with Edge Impulse based on thermal images, with data sent over cellular to the cloud via the Notecard.
Find more projects featuring Edge Impulse and Blues Wireless on Hackster.io.
Edge Impulse Swag
*Version awarded is based on winners locations