Machine Learning: The Future from the Perspective of Model Building – CIO Applications

A good example of this is using specific gestures to raise or lower the volume or to changetracks--instead of pushing buttons to navigate your cars entertainment system. Some companies, such as Arcturus Networks, are building software modules for surveillance cameras and then selling them to camera manufacturers for integration into their end products. These are just a few examples of the types of companies that are popping up with specialties related to application functions.

Everybody can talk about a neural network, but it is essential to understand what it really means and the value it brings to finding other ways of solving problems

The main driver for us is figuring out how to make open-source technologies easier for our customers to use. The NXP eIQ Machine Learning Software Development Environment is continuously expanding to include model conversion for a wide range of NN frameworks and inference engines, such as TensorFlow Lite and Glow (the PyTorch Compiler). There are also open-source technologies from Arm, such as Arm NN, that will enable higher performance machine learning on ArmCortex Aprocessors. We are even using open-source inference engines to enable machine learning accelerators in our devices. Case in point is our new device called the i.MX 8M Plus. This is our first applications processor featuring an integrated machine learning accelerator that delivers two to three times more performance than NXP devices without it. And, integrating higher performance machine learning capability with acceleration is one of the emerging trends in the industry.

Whats Next?

The problem is that machine learning, or AI in general, is such a fast-growing area. The good and bad is that there have been far too many different technologies to keep up with and for us to support. Moving into the future, the technology around today will either be merged or well start to see more de facto standards. For instance, TensorFlow is something thats not going to go away and represents a significant share of the machine learning developers. On the other hand, PyTorch has quickly been gaining in popularity, especially in the academic community. Other similar technologies created with a specific purpose in mind may be useful, but industry adoption is low. These outliers may merge or disappear in the future. This is perhaps one of the main trends that I see moving forward.

A few years down the road, machine learning will become a de facto standard, and youll see it implemented in a majority of devices because people will realize that its not magicand the good tools that are already available to make it work are getting better. And, you dont have to be a data scientist or an expert in understanding neural network technology to integrate machine learning into your platform. And thats one area where we also spend a lot of time at NXP -- how do we make it easier for customers to deploy their machine learning models on our devices. We see both performance improvements and memory size reductions as the technology is becoming more optimized, so thats going to be a significant way forward.

Piece of Advice

As previously mentioned, we have developed a technology called eIQ for edge intelligence. I encourage people to check it out, try walking through some of the application examples, and experience machine learning in action. Like most of us, if youre trying to learn more about this technology, there are many good YouTube videos and an abundance of articles you just have to spend the time filtering through them. But you can learn a lot by what people have posted online: everything from the basics of what is a neural network, how to train a neural network, how to make it more performance efficient and more accurate, and so on. Theres plenty of information available for people who are starting. One exciting thing about machine learning, which applies to other technologies as well, is that the more you learn about it, the more you realize you dont know. Everybody can talk about a neural network, but understanding what it really means and its value in solving problems is essential to unlocking machine learnings extraordinary potential.

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Machine Learning: The Future from the Perspective of Model Building - CIO Applications