neuromorphic tagged posts

Researchers have developed a new kind of nanoelectronic device that could dramatically cut the energy consumed by artificial intelligence hardware by mimicking the human brain. The researchers, led by the University of Cambridge, developed a form of hafnium oxide that acts as a highly stable, low-energy “memristor”—a component designed to mimic the efficient way neurons are connected in the brain. The results are reported in the journal Science Advances.

Current AI systems rely on conventional computer chips that shuttle data back and forth between memory and processing units. This constant movement consumes large amounts of electricity, and global demand is exploding as AI adoption expands across industries.

Brain...

Robots and self-driving cars could soon benefit from a new kind of brain-inspired hardware that can allegedly detect movement and react faster than a human. A new study published in the journal Nature Communications details how an international team built their neuromorphic temporal-attention hardware system to speed up automated driving decisions.

The problem with current robotic vision and self-driving vehicles is a significant delay in processing what they see. While today’s top AI programs can recognize objects accurately, the calculations are so complex that they can take up to half a second to complete...

This is the memristor array situated on a circuit board.
Credit: Mohammed Zidan, Nanoelectronics group, University of Michigan.

A new way of arranging advanced computer components called memristors on a chip could enable them to be used for general computing, which could cut energy consumption by a factor of 100. This would improve performance in low power environments such as smartphones or make for more efficient supercomputers, says a University of Michigan researcher. “Historically, the semiconductor industry has improved performance by making devices faster...