Signal processing is key to embedded Machine Learning

When we hear about machine learning - whether it's about machines learning to play Go, or computers generating plausible human language - we often think about deep learning. Lots of unstructured data gets thrown in a complex neural network with billions of parameters, and after a very expensive training stage the model learns the task at hand. But this is not always a desirable approach. One of the most interesting places where we can run machine learning is on embedded or IoT devices. These devices already handle a vast amount of high-resolution sensor data, but often need to send the sensor data to the cloud to get analyzed.