JULY 2020CIOAPPLICATIONS.COM9A strategy that is Steering your Business GrowthThe 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. What's 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 we'll start to see more de facto standards. For instance, TensorFlow is something that's 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 trendsthat I see moving forward. A few years down the road, machine learning will become a de facto standard, and you'll see it implemented in a majority of devices because people will realize that it's not magic­and the good tools that are already available to make it work are getting better. And, you don't have to be a data scientist or an expert in understanding neural network technology to integrate machine learninginto your platform. And that's 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 that's going to be a significant way forward. Piece of AdviceAs previously mentioned, we have developed a technology called eIQTM for edge intelligence. I encourage people to check itout, try walking through some of the application examples, and experience machine learning in action. Like most of us, if you're 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. There's 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 don't 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 learning's extraordinary potential. 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
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