A baby learns to crawl, walk and then run. We are in the crawling stage when it comes to applying machine learning - Dave Waters
In this tech world, we use machine learning models in anything we do. Scrolling through a web page, getting notifications, checking the weather, booking a flight, and more depend on these models. Most of these models run massive data centers with CPUs and GPUs. Machine learning is employed at every stage, and hence it requires computer systems that are fast enough to handle it. However, training these models and deploying them is expensive and consumes a lot of power and time but is so important.
TinyML is a field of study in Machine learning that employs models that can run on small systems that are low-powered, called microcontrollers. When a standard CPU consumes somewhere between 65 to 85 watts, a standard GPU consumes anywhere between 200 to 500 watts, and a microcontroller consumes power in milliwatts or microwatts. It cuts down on power about a thousand times. Tiny ML enables low power, low latency, and low bandwidth model inference at small IoT devices that can run on batteries for months or years.
How Beneficial is TinyML?
Following are the benefits of TinyML:
- Saves Energy: Microcontrollers use low power and can run on batteries for a long time. It saves energy and makes it cost-effective.
- No Delay in Data Path: Data is not required to be sent to the server every time as the devices used are small. It implies it takes less time and no delay for the data packet to travel and reduces the output latency.
- No Internet necessary: Since data isn’t sent to the server now and then, the process happens in less bandwidth and sometimes without internet. Hence it is not dependent on the connection.
- Data Privacy: Since data isn’t sent to external users or websites, data stays secure, and privacy is protected.
Applications of TinyML
Tiny ML has been successful in running on edge devices and offers many solutions. It can answer audio commands to execute actions through chemical interactions. Google Assistant and Alexa are some examples of TinyML. The devices are always on and analyze your voice to detect the wake word. Some other applications that can be a good or bad idea are:
TinyML, when used on low-powered devices, can detect faults in a machine ahead of time constantly. It implies maintenance based on predictions. One such example is introducing an IoT device by an Australian start-up, Ping Services, that monitors wind turbines by attaching itself to the turbine’s exterior. It alerts the authorities if it senses any potential issue or malfunction.
TensorFlow Lite's tool enables farmers to detect diseases in a plant when they take a picture of it. It works on any device and needs no internet connection. The process allows for the protection of agricultural interest and is a crucial requirement for remote farmers.
A project called the Solar Scare Mosquito uses TinyML to stop the spread of diseases like dengue, malaria, etc. It runs on solar power, detects mosquito breeding conditions, and signals the water to prevent mosquito breeding.
Tiny ML-powered devices monitor whales in real-time and alert them during strikes in busy shipping lanes.
Deployment of TinyML
Following are the steps that make the deployment of TinyML easier:
Choose the Relevant Hardware
To deploy TinyML models on edge devices, Arduino Nano is the most used and suggested hardware. It has color, audio, temperature, humidity, pressure, and so many other sensors. It can use it in most applications, has a digital mic and a Bluetooth low energy module. The microcontroller has enough frequency to run TinyML models with 1MB of program memory.
Machine Learning Tools
Tools help TinyML models for easy deployment as they provide the most community support. One such example in the article is TensorFlow Lite. CoreML and PyTorch Mobile are two more competitors of TensorFlow Lite. Improvements are made in these frameworks to support complex ML models. Such frameworks have functioned as detecting objects in an image, responding intelligently like a chatbot, and offers recommendations based on customer’s behavior.
Learning Resources for Easy Deployment
Once the model is on the floor, it's essential to learn about it and impart training for the same. Few materials like “TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low Power” by Pete Warden are beneficial. Some of the relevant courses are also available online.
The Big Future of the TinyML
According to a forecast, by 2030, an approximate number of 2 Bn devices will reach the market through TinyML techniques, benefiting the economy by being cost-effective and creating intelligent devices. In economic terms, TinyML can get more than $70 Bn in the next five years. TinyML is here to change the scenario of applications in IoT devices and change the future of intelligent devices.
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