Markus Schaefer’s Mercedes BRN mentioned.

Markus Schäfer’s PostMarkus SchäferMember of the Board of Management of Mercedes-Benz Group AG, Chief Technology Officer, Development & Procurement1yMy new “In the Loop” series kicks off with #Neuromorphic Computing – the clear winner of my poll a few weeks ago.For those unfamiliar, this highly significant field of computing strives to emulate the multi-tasking of the human brain. Traditional microprocessors function sequentially. However, as the complexity and scale of calculations sores, this way of doing things is rapidly running out of road.The idea is not new, but trying to “put a brain on a chip” is a mammoth task. To put it into figures: the human brain has 86-100 billion neurons operating on around 20 watts. Current neural chips from leading developers such as BrainChip and Intel Corporation contain around 1 million neurons and consume roughly 1 watt of power.So, you see, despite impressive advances, there is still a very long way to go. Neuromorphic computing goes well beyond chip design and includes a specific kind of artificial neural network called #spikingneuralnetworks (SNN). They consume far less energy because the neurons are silent most of the time, only firing (or spiking) when needed for events. Together with intense parallel execution on neuromorphic chips, the new processing principles require us to go beyond the application of existing #AI frameworks to neuromorphic chips. We have to fundamentally rethink the algorithms that ultimately enable future AI functions in our cars, gathering joint inspiration from machine learning, chip design and neuroscience. Our experts are working closely with our partners to examine their potential in new applications. The thing is, even a tiny fraction of the thinking capacity of the human brain can go a long way in several fields that are extremely relevant to automotive applications. Examples include advanced driving assistance systems #ADAS as well as the on-board analysis of speech and video data, which can unlock major advances in how we communicate with our cars. We already made some interesting findings here with our VISION EQXX, where we applied neuromorphic principles to the “Hey Mercedes” hot-word detection. That alone made it five to ten times more energy efficient than conventional voice control. As AI and machine learning take on an increasingly important role in the software-defined vehicle, the energy this consumes is likely to become a critical factor. I’ll touch on our latest findings in an upcoming “In the Loop” and tell you my thoughts on where this is taking us. In the meantime, for those of you interested in reading up on neuromorphic computing, check out the slider for my recommended sources. I’ve graded them to ensure there’s something for everyone, from absolute beginner to true geeks.