Mercedes EV Concept Car Incorporates Neuromorphic Computing

Article By : Sally Ward-Foxton

BrainChip's Akida neuromorphic chip powers keyword spotting in the Mercedes Vision EQXX.

The Mercedes Vision EQXX concept car, promoted as “the most efficient Mercedes-Benz ever built,” incorporates neuromorphic computing to help reduce power consumption and extend vehicle range. To that end, BrainChip’s Akida neuromorphic chip enables in-cabin keyword spotting as a more power-efficient way than existing AI-based keyword detection systems.

As automakers shift their focus to electric vehicles, many are struggling to squeeze every last volt from a single battery charge. The need to reduce power consumption in vehicle electronic systems has therefore become critical to extending EV range.

Touting Vision EQXX as “a car that thinks like you,” Mercedes promises range of more than 1,000 km (about 620 miles) on a single charge.

“Working with California-based artificial intelligence experts BrainChip, Mercedes-Benz engineers developed systems based on BrainChip’s Akida hardware and software,” Mercedes noted in a statement describing the Vision EQXX. “The example in the Vision EQXX is the “Hey Mercedes” hot-word detection. Structured along neuromorphic principles, it is five to ten times more efficient than conventional voice control,” the carmaker claimed.

(More on the Mercedes Vision EQXX here).

Mercedes Vision EQXX uses neuromorphic computing to save power
The Mercedes Vision EQXX uses neuromorphic computing to save power. (Source: Mercedes)

That represents validation of BrainChip’s technology by one of its early-access customers. BrainChip’s Akida chip accelerates spiking neural networks (SNNs) and convolutional neural networks (via conversion to SNNs). It is not limited to a particular application, and also run person detection, voice or face recognition SNNs, for example, that Mercedes could also explore.

Mercedes neuromorphic
Interior of the Mercedes Vision EQXX concept car. (Source: Mercedes)

“Although neuromorphic computing is still in its infancy, systems like these will be available on the market in just a few years,” Mercedes said. “When applied at scale throughout a vehicle, they have the potential to radically reduce the energy needed to run the latest AI technologies.”

Separately, another early BrainChip customer, Information Systems Laboratories, is developing AI-based radar for the U.S. Air Force Research Laboratory. The radar is also based on the Akida chip.

Production boards

Following up on two development kits it announced in October (an x86 Shuttle PC kit and an Arm-based Raspberry Pi kit), BrainChip is now offering its Akida chip on a Mini PCIe board in high volumes, targeting system integrators for smart home, health, city and transportation applications.

“If someone wanted a high volume of boards, we can support that; if they want to buy some boards to ramp up for silicon, they can do that as well,” said Rob Telson, BrianChip vice president of worldwide sales. “This isn’t a test kit or a reference kit, it’s meant for production.”

“Being able to launch the board for production and have this commercial stack available [would enable] a simple path for anyone from hobbyists to companies who wants to just plug 10 boards in for specific applications, all the way through to licensing IP,” Telson added. “It gives them a lot of flexibility to access Akida in its current version.”

BrainChip is also offering PCB design layout files and bill-of-materials information to system integrators using the Akida chip as a standalone accelerator or as a co-processor.

Akida silicon, IP, Mini PCIe boards and development kits are available now.

This article was originally published on EE Times.

Sally Ward-Foxton covers AI technology and related issues for and all aspects of the European industry for EE Times Europe magazine. Sally has spent more than 15 years writing about the electronics industry from London, UK. She has written for Electronic Design, ECN, Electronic Specifier: Design, Components in Electronics, and many more. She holds a Masters’ degree in Electrical and Electronic Engineering from the University of Cambridge.


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