Photo by Nubia Navarro (nubikini)
 

Machine learning is used by all types of organizations to process and analyze large datasets. TinyML (Tiny Machine Learning) is one form of machine learning API that is growing in popularity for its low-power capabilities. TinyML is quickly establishing itself as one of the best options for getting to grips with machine learning from a beginner’s level. 

This article will provide an overview of what TinyML is, its use cases, and why it is becoming more popular. 

 

What is TinyML?

 

Tiny Machine Learning (TinyML) is an optimized machine learning technique that allows ML models (software) to run on embedded systems that use very low-power microcontrollers.

An embedded system is made of computer hardware and software designed for a specific function. They are developed for one purpose only, which is why they differ from typical computer systems such as laptops, PCs, tablets, and smartphones. An example of an embedded system would be an electronic calculator or an ATM. 

TinyML enables machine learning on microcontrollers and Internet of Things (IoT) devices in a very optimized way, meaning vast amounts of data can be leveraged and analyzed while using very little power. 

Now let’s look at microcontrollers in more detail.

 

An Overview of Microcontrollers

 

Microcontrollers consist of the computer processor, RAM, ROM, and Input/Output (I/O) ports of an embedded system, the usual hardware setup that allows the embedded system to process software. 

The key benefits of using microcontrollers are:

• Low-power - Microcontrollers are low-power hardware, requiring just milliwatts and microwatts to function. This means that microcontrollers consume around a thousand times less power than a regular computer system. 
• Low price - Microcontrollers are very cheap, with over 28 billion units shipped in 2020 alone. 
• Multifunctional use - Microcontrollers can be used in all devices, gadgets, and appliances. 

 

The Advantages of TinyML

 

There are three main advantages of using TinyML:

1. Data can be processed with low latency - Because TinyML allows for on-device analytics without any connection to a server, embedded systems can process data and produce an output with almost no delay (low latency). 
2. No connectivity required - The device does not need an internet connection for the TinyML model to work. 
3. Privacy of data - As all the data is contained within the device with no connectivity, the risk of any data being compromised is extremely low. 

 

TinyML Implementation

 

There are a few popular machine learning frameworks that support TinyML.

•  Edge Impulse is a free machine learning development platform for edge devices (the hardware that provides an entry point into a network). An example of this could be a router or routing switch. 
• TensorFlow Lite is a library of tools that allows developers to enable on-device machine learning on embedded systems, edge devices, and other stand-alone devices. 
• PyTorch Mobile is an open-source machine learning framework for mobile platforms and is compatible with TinyML. 

 

Why is TinyML Becoming more Popular?

 

Internet of Things (IoT) networks are gaining prevalence across various industries. As such, more edge devices are required to bridge power sources and network endpoints.; TinyML delivers these requirements cost-effectively. 

As discussed, TinyML works on low-power microcontrollers that do not require internet connectivity. This allows the device to process and respond to data in real time without using significant resources.

TinyML models can be used as an alternative to a cloud environment, reducing costs, using less power, and offering more data privacy. Everything is processed on the individual device without latency, ensuring impressive connection and processing speeds. 

TinyML is becoming more popular in 2022, and researchers expect further growth in the future. Technology research and strategic guidance group ABI predicts that 2.5 billion devices using TinyML systems will be shipped in 2030. 

The advantages of TinyML range from instantaneous analytics to no latency, making it an obvious choice in a world that relies on speed. Furthermore, the local processing of data means sensitive information is better protected from cybercriminals when compared to centralized data centers. 

 

The Challenges TinyML Faces

 

Although the benefits and potential of TinyML are clear, it is not without its challenges that could pose some issues for developers. 

• Limited memory capacity - Embedded systems that use TinyML are limited to megabytes and sometimes kilobytes of internal memory. This places significant restrictions on how complex TinyML models can be, which is why there are only a small number of frameworks that can be used for TinyML development. 
• Troubleshooting can not be conducted remotely - As TinyML models only run on the device locally, it is much harder for developers to perform troubleshooting to determine and fix any problems. This is where a cloud environment offers an advantage. 

 

How is TinyML used in Practical Situations?

 

TinyML can be effectively applied to many different industries, pretty much any that use IoT networks and data. Below are several industries where TinyML has been used to power operations. 

 

Agriculture

 

TinyML devices have been used to monitor and collect real-time crop and livestock data. A market-leading example of this was developed by Imagimob, an edge AI product company from Sweden. Imagimob has worked with 55 organizations across the European Union to understand how TinyML can provide cost-effective livestock and crop management. 

In a study, two tractors were fitted with Dialog IoT Kit (Bosch sensor) devices and Android phones to collect data. This data was either logged in real-time by the tractor operator or by analyzing the smartphone’s video stream. 

Imagimob’s AI software was installed on the sensors, batteries, and long-range radio devices to improve this method and allow the farmer to monitor crops using the accelerometer and gyroscope sensors, sending almost real-time data over the network. 

 

Retail

 

TinyML has been adopted by the retail industry to monitor inventories, sending alerts whenever stock is running low and needs to be reordered.

 

Healthcare

 

TinyML can also be used in real-time health monitoring equipment, providing patients with improved and more personalized care. For example, hearing aids are battery-powered hardware that uses a low-power microcontroller, meaning they need minimal resources to function effectively. Using TinyML, researchers were able to reduce the latency of these devices without any loss in performance. 

In the future, implementing TinyML devices could spread to all industries, helping businesses manage finances, process invoices, better work with clients, collect and analyze data, and more. 

 

Manufacturing

 

TinyML can be used to power predictive maintenance tools to help minimize any downtime and the added costs resulting from any equipment being out of action. 

 

Conclusion

 

TinyML is growing in popularity across various industries that use Internet of Things (IoT) devices. This is because TinyML models can run on microcontrollers to provide specific functions while using very little power.

Despite being a low-power solution, TinyML devices can process data with low latency and without needing an internet connection. This lack of network connection also helps to protect collected data from hackers. 

TinyML is already being used effectively by the agriculture, manufacturing, and healthcare industries, and its popularity is predicted to grow further.

 
 
Nahla Davies is a software developer and tech writer. Before devoting her work full time to technical writing, she managed — among other intriguing things — to serve as a lead programmer at an Inc. 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.