Machine learning (ML) is a method of processing raw data to turn it into information that helps to aid the function of an application or device.
The rules of machine learning programs aren’t necessarily predetermined by a requirement, unlike most computer programs. Machine learning utilises specific algorithms in order to learn rules from the raw data in a process known as training.
Why is embedded machine learning (ML) helpful?
Usually, an engineer would design an algorithm that takes an input and applies various rules to return an output that enables a software to function correctly. This means the algorithm is already planned by the engineer and implemented within the program code.
To be able to predict errors within the machine, an engineer would then need to understand which parts of the data reference the issue and then rewrite the code that deliberately checks for errors.
This process makes it more difficult to solve issues, as it is hard to know the exact piece of code that causes the issues present and remove this or test it, but machine learning changes this process. The training process results in a model. This model can be tested against subsets of the training data, and if results are inadequate a new model can be generated.
How are machine learning (ML) programs created?
When creating an ML program, the engineer will need to collect a lot of data that can be used when training the program. They can then feed that data into the algorithm and let the program discover the rules. This can be beneficial, as engineers could create programs that can make predictions based on complex data sets without needing to understand all the data themselves. The machine will itself discover edge cases in the data and may resolve them without further engineering input.
During the training process, the machine learning algorithm will build a model of the system based on the data it is provided with. Further data can then be run through this model to make predictions. This process is called inference.
Where can machine learning help?
Embedded machine learning (EML) is extremely useful for problem-solving, especially if those problems involve pattern recognition or if patterns are too complex for human understanding or for a human observer to identify.
A machine learning algorithm is helpful in turning raw, high-bandwidth data into usable knowledge.
What are the limitations of machine learning?
Although ML programs and algorithms are great tools, they do have limitations, including:
- They only estimate and offer approximations rather than exact answers.
- Machine learning models can be extremely expensive to run (CPU and RAM).
- Training a machine learning program can be time-consuming and expensive to undertake.
- Although tempting to apply an ML program to solve any problem, it is much more cost-effective to try to solve the problem at hand without the use of an ML algorithm.
What are the advantages of embedded machine learning?
Advancements in algorithm designs now make it possible to run detailed machine learning programs on small microcontrollers. Embedded machine learning, such as TinyML, is a part of machine learning that works with embedded systems.
There are numerous advantages to running machine learning on embedded devices, such as:
- Machine learning algorithms on embedded devices can extract key information from data sets that might otherwise be inaccessible due to bandwidth constraints.
- Embedded machine learning models are able to respond in real-time to input, which enable applications which would not be viable if they were dependent on network latency.
- Processing data on embedded machine learning systems helps to avoid the costs of transmitting data over a network and processing it in the cloud.
- Systems that are controlled by on-device models do not depend on a connection to the cloud.
- When data is processed on an embedded system and doesn’t have to be transmitted to the cloud, there is greater protection and data privacy.
Machine Learning and AI
Machine learning is a subfield of AI that looks at developing and studying statistical algorithms that can learn from data to make unseen information understandable, which means that tasks can be performed without instruction.
Machine learning is often synonymous with cloud computing, which includes powerful servers processing lots of data. This data is then used to perform further tasks.
However, not all machine learning can be completed in the cloud and sometimes machine learning applications must perform processing locally in order to efficiently complete a task. This usually means placing lots of computing power into small devices
The future of machine learning
In recent years, research around machine learning has come very far and now machine learning algorithms can be run on smaller devices such as tiny microcontrollers.
This has led to embedded machine learning (TinyML) performing tasks on smaller computers.
Whilst running embedded machine learning on smaller computers isn’t new, having the ability to run them on microcontrollers that are less powerful and complex than small board computers and contain less advanced processors, opens up much more opportunity to foster a new generation of AI-powered electronics.
