New-Tech Europe | February 2019

Artificial Intelligence: It's all about the Cloud or it’s on the Edge ? Ł ukasz Grzymkowski, Arrow Electronics

Buzzwords like Artificial Intelligence, Machine Learning, Deep-learning have all in recent years gained a lot of attention. This is in many parts thanks to the large, internet- based corporations that utilize them for extremely interesting tasks like image or speech recognition, natural language processing and more while Most of us use such systems on a daily basis. When thinking about Machine Learning, the first thing that comes to our mind is the cloud hosted on enormous data centers with thousands of servers. For many applications this is the standard and has been for many years. However, with recent increase in hardware availability and performance, thanks to the advent of the Internet of Things, and decrease in cost, a vast range of use cases are moving from the cloud directly to the

edge. In this paradigm shift, the node devices are becoming more autonomous as the intelligence shifts closer to the field and away from the cloud, where the events take place. This has already enabled very interesting applications, like autonomous drones, ADAS systems in automotive, smart mobile robots and this is certainly just the beginning. In the following article, we will provide an overview of what a system designer must consider when working on an artificial intelligence in the edge solution. The typical flow comes down to understanding the task to be solved, choosing the algorithm, training and deploying a model for inference. The Goal: defining the problem When working on any solution,

probably the most important, and surprisingly often neglected, step is to identify the problem that we are trying to solve. Truly understanding the issue is crucial to choosing the right angle and technology to tackle it. Is what you are trying to address a complex classification task that requires a deep neural network with many hidden layers? Machine learning is not limited to deep learning and neural networks only. There are plenty of so-called "classical" machine learning algorithms, for example k-means, support vector machines, statistical models, that are often less resource-intensive and may in fact be an optimal solution. It is there important to experiment and be able to fail-quickly in order to move forward with a more appropriate approach. That said, the deep learning is what has been

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