As machine learning and embedded systems become increasingly important in today’s society, many people are beginning to explore the use of machine learning in embedded systems. This approach can overcome many challenges that may arise when using traditional machine learning. This article will introduce some advantages of this approach.
Cost Savings Bring New Opportunities
Many decision-makers combine machine learning with embedded systems after realizing their potential economic benefits. Since the processing occurs directly on the device, there is no need to transfer data to the cloud, which can often save money.
In addition to reducing data transfer costs, they can also use machine learning in embedded systems with fewer computing resources. This may mean that they can start exploring opportunities without making large investments.
However, it is important to remember that the combination of machine learning and embedded systems does not guarantee cost savings. When people seriously consider which parts of their existing processes have the highest financial demands and how they currently use the cloud, they are more likely to save costs.
Using machine learning on embedded systems typically does not eliminate the costs of cloud computing. But it can help companies reduce their reliance on the cloud, as data processing is done directly on the device rather than in the cloud.
Professionals looking to use machine learning in embedded systems to save costs should shrink areas where they currently spend too much and areas they believe are worth saving. They must also remember that the benefits of cost savings may not be immediately visible. Determining the best way to incorporate machine learning into their systems often takes time, but those involved often find that their efforts are rewarded with patience.
More Practical Applications
Many people use machine learning to enhance decision-making capabilities. Because data processing can be done directly on embedded systems, people often find that they can obtain useful information almost immediately.
This is undoubtedly beneficial for industries like healthcare, where prolonged decision-making times can lead to catastrophic complications for patients. In all industrial applications, people face constantly changing conditions, making it easy to see its advantages.
Hefring Marine is a Finnish company focused on developing embedded systems for marine vessels. The company has adopted machine learning technology in a product called Intelligent Maritime Assistance System (IMAS). IMAS uses algorithms to provide recommendations for the optimal speed for vessels to enhance safety and fuel efficiency. Wave impact measuring devices are also part of this system, which will soon be equipped with vibration monitoring capabilities. The company states that these features can reduce adverse impacts on passengers and vessels.
The company’s statistics indicate that the wave impact measuring feature can reduce severe impacts by 70%. It can also help users reduce fuel consumption and carbon emissions by 25%. Additionally, it can bring more benefits to individual travel, saving up to 20% on insurance premiums.
These are excellent examples of what can be expected when using machine learning in embedded systems. However, any leader considering purchasing commercial products must carefully review all possibilities against their plans, based on how they will use these solutions. Once it is confirmed that those technologically advanced products are well-suited for their potential applications, people can expect to achieve ideal results from these products.
Significantly Reduced Latency
People across various industries are continually trying to develop machines that can increase output and maintain high levels of productivity. For example, some companies use dot peen marking machines to add serial numbers, barcodes, QR codes, etc., to their products. Such devices can add four to eight characters to a surface on average per second.
It is not surprising that industry leaders are interested in how machine learning can work directly on embedded systems, as they seek growth through process improvement. As they become familiar with the various potential values, the reduction of latency quickly becomes apparent.
Since data processing can occur directly on the devices collecting information, there is almost no latency. As processing speeds decrease, people can access and evaluate data almost instantaneously.
In one case, researchers developed a method to accelerate the training of machine learning algorithms directly on mobile devices. Their findings showed a 28.4% reduction in latency compared to traditional CPU-based algorithm preparation methods. Although those involved acknowledge that training algorithms on devices still faces many challenges, the strong results they have achieved encourage people to believe that overcoming these challenges will yield even more benefits.
Better Support for Sustainability
One of the downsides of the machine learning boom is that training and running algorithms require significant energy consumption. However, as people research machine learning in embedded systems more, some have found potential solutions to this issue. Most of this progress relates to an emerging field called tinyML. As its name suggests, it focuses on using machine learning in low-power applications, including embedded systems.
These use cases process data on the device or at the edge, significantly reducing the required computing power and data center storage space. Many tinyML applications are also more sustainable, as they are trained to only transmit data of interest rather than all collected information. In addition to saving computing resources, this approach also reduces the time required for humans to extract useful insights.
tinyML systems also benefit microcontrollers commonly used. This supports sustainability because it requires fewer raw materials for hardware. However, most sustainability outcomes related to machine learning in embedded systems concern the ability to process more data without needing extensive computing resources.
This benefit can also help those who want to use machine learning but cannot afford expensive equipment. For example, farmers might use tinyML systems for automatic pest detection in crops. It allows them to use a more targeted approach, applying pesticides only when and where necessary rather than over large areas.
Some tinyML applications can also directly support sustainability. One example is a tracker on an elephant collar that captures real-time location and image data and identifies potential dangers. Accelerometers and audio sensing capabilities can help researchers gain further insights into elephant behavior, including their emotions and the frequency or speed of their movements.
How Will You Use Machine Learning in Embedded Systems?
These are some of the main advantages of building machine learning capabilities in embedded systems. These are just some of the benefits you may encounter, and there are other advantages you may enjoy regarding how your business or clients handle specific processes. Remember, everything you learn during development and deployment can help you achieve better results in your future work.
Author：Emily Newton， Source：EDN Sister Site EEWeb
Original Reference: 4 Benefits of Machine Learning in Embedded Systems, compiled by Ricardo Xie.
Copyright Notice: This article is an original article from Electronic Technology Design, all rights reserved, and reproduction is prohibited.
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