Neuromorphic Engineering stands at the crossroads between technological and biological evolution. Neuromorphic designs aim to mimic the functioning of our brain’s neurons and synapses (developed over millions of years of evolution) in order to unlock orders of magnitude improvements in both computing capability and power efficiency. The abysmal difference in efficiency can’t be understated: the world’s most powerful supercomputer, Japan’s Arm-based Fugaku, consumes an average of 28 million watts. Yes, it can solve problems that our brain can’t; but the opposite is also true. Modern computers still lack the spark of creativity that comes (at least partially) from the ability to combine known information in novel ways – while consuming an estimated 10 W to 20 W of power. Memristors, with their capability to both process and store data, are key to achieve comparable levels of efficiency.
Research Paves the Way for Honey-Based Neuromorphic Computing: The researchers hope their research paves the way for biodegradable, sustainable, organic-based computing systems that are orders of magnitude more efficient than conventional computing architectures.
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.
Mercedes Applies Neuromorphic Computing in EV Concept Car: 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.
“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.”
Designed flexible, wearable electronics for health sensing
Flexible, wearable electronics are making their way into everyday use. One day, this technology could be used for precision medical sensors attached to the skin, designed to perform health monitoring and diagnosis. Such a skin-like device is being developed in a project between Argonne and The University of Chicago’s Pritzker School of Molecular Engineering.
Worn routinely, future wearable electronics could potentially detect possible emerging health problems — such as heart disease, cancer or multiple sclerosis — even before obvious symptoms appear. The device could also conduct a personalized analysis of the tracked health data while minimizing the need for its wireless transmission.
The device relies on neuromorphic computing, an artificial intelligence (AI) technology that mimics how the brain works by training on past data sets and learning from experience. Its advantages include compatibility with stretchable material, lower energy consumption and faster speed than other types of AI.
Read more about these stretchable electronics.
Developed a material for a computer that mimics the brain
A team of researchers from Argonne and Purdue University has developed “neuromorphic” materials — electronic components that function similarly to the human brain. These materials can “learn” new information and reconfigure their circuitry in a brain-like way.
Combining this ability with the power of AI, computers could carry out complex tasks faster and more accurately, while expending much less energy. One example is in interpreting complex medical images.
The key material in the new device is referred to as a perovskite nickelate (NdNiO3). The team infused this material with hydrogen and attached electrodes that allow electrical pulses to be applied at different voltages. By applying a certain voltage, the researchers could control the movement of hydrogen in the nickelate, which determines the electronic properties of the material.
Read more about this neuromorphic material.
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