TinyML – The Future of Intelligent Edge Solutions
In a world where artificial intelligence (AI) traditionally requires large amounts of data processing and power, TinyML has emerged as a revolutionary technology. TinyML stands for “Tiny Machine Learning” and refers to the ability to run machine learning models on extremely small and energy-efficient devices. This technology enables advanced data analysis directly on microcontrollers and edge devices without the need for a constant cloud connection.
Mjølner’s Embedded Department recently invited a promising Danish researcher, Ph.D. student Emil Njor from DTU, to give a talk and share his knowledge in the field of TinyML. It led to an exciting technological discussion, covering tools such as compilers, models, algorithms, data, and hardware. Emil emphasized how the technology can be applied across sectors and how developments in supporting tools make it easier to develop TinyML based on existing systems. One very concrete outcome of Emil’s Ph.D. work is a framework for creating better datasets to evaluate the quality of a TinyML algorithm.
What is TinyML?
Research in TinyML focuses on taking well-known algorithms and optimizing them so that they require minimal processing power and energy. This makes it possible to implement intelligent features in devices that were previously too limited to handle AI computations. This opens the door to a range of new applications within IoT, industrial systems, health technology, and smart products.
One of the major advantages of TinyML is its ultra-low power consumption, meaning that devices can run AI solutions for months or even years on a single battery charge. This is a game-changer for IoT and embedded systems, where battery life is often a significant challenge.
Embedded and TinyML: Make small devices smarter
Embedded development at Mjølner is about creating efficient, user-friendly, and optimized systems for specialized hardware. TinyML fits perfectly into this context because it allows for the integration of machine learning directly onto small, resource-constrained devices without the need for a cloud connection or with minimal cloud dependency.
Why Should Companies Consider TinyML?
At Mjølner, we already have projects and clients working with TinyML, but we also find that many companies are not yet ready to fully implement it. As a result, most of our client engagements start with a technology briefing, which helps identify the company’s competencies for launching TinyML projects.
Starting TinyML projects requires in-house technical expertise, but it also opens up exciting opportunities. With specialized knowledge in embedded resources, compiler optimization, and hardware customization, companies can create more efficient and innovative solutions, giving them a significant competitive advantage.
4 advantages with TinyML:
- Reduce the need for constant cloud connectivity, thereby improving privacy and security, as data does not necessarily need to be transferred to the cloud.
- Minimize latency, as AI computations are performed directly on the device.
- Build robust systems that can operate even in offline environments.
- Save energy and resources, which impacts both operation and sustainability.
We Asked the Experts: Opportunities and Limitations of TinyML
The developers in our embedded department have an in-depth knowledge of TinyML and have worked with several clients to introduce it into their projects. As a result, they are well aware of the opportunities and limitations the technology brings.
The Future of TinyML
We are just at the beginning of TinyML’s potential. As the technology matures and more companies gain access to the necessary competencies, we will witness an explosion of new application possibilities. For businesses looking to stay ahead, now is the time to begin exploring TinyML and the advantages it can bring.
At Mjølner, we closely follow developments and help companies understand and implement TinyML in their solutions. Want to learn more about how TinyML can make a difference for your business?