Neuromorphic semiconductor chip corrects errors autonomously

Existing computer systems have separate data processing and storage devices, making them inefficient for processing complex data like AI. A KAIST research team has developed a memristor-based integrated system similar to the way our brain processes information. It is now ready for application in various devices including smart security cameras, allowing them to recognize suspicious activity immediately without having to rely on remote cloud servers, and medical devices with which it can help analyse health data in real time.

KAIST President Kwang Hyung Lee announced that the joint research team of Professor Shinhyun Choi and Professor Young-Gyu Yoon of the School of Electrical Engineering has developed a next-generation neuromorphic semiconductor-based ultra-small computing chip that can learn and correct errors on its own.

Scanning electron microscope (SEM) image of a computing chip equipped with a highly reliable selector-less 32 x 32 memristor crossbar array (left). Hardware system developed for real-time artificial intelligence implementation (right). Image credit: KAIST

What is special about this computing chip is that it can learn and correct errors that occur due to non-ideal characteristics that were difficult to solve in existing neuromorphic devices. For example, when processing a video stream, the chip learns to automatically separate a moving object from the background, and it becomes better at this task over time.

This self-learning ability has been proven by achieving accuracy comparable to ideal computer simulations in real-time image processing. The research team’s main achievement is that it has completed a system that is both reliable and practical, beyond the development of brain-like components.

The research team has developed a memristor-based integrated system that can adapt to immediate environmental changes, and has presented an innovative solution that overcomes the limitations of existing technology.

Background and foreground separation results of an image containing non-ideal characteristics of memristor devices (left). Real-time image separation results through on-device learning using the memristor computing chip developed by the research team (right). Image credit: KAIST.

At the heart of this innovation is a next-generation semiconductor device called a memristor. A memristor is a compound word of memory and resistor that describes a next-generation electrical device whose resistance value is determined by the amount and direction of charge that has flowed between the two terminals in the past. The variable resistance characteristics of this device can replace the role of synapses in neural networks, and by utilising it, data storage and computation can be performed simultaneously, just like our brain cells.

The research team designed a highly reliable memristor that can precisely control resistance changes and developed an efficient system that excludes complex compensation processes through self-learning. This study is significant in that it experimentally verified the commercialisation possibility of a next-generation neuromorphic semiconductor-based integrated system that supports real-time learning and inference.

This technology will revolutionise the way artificial intelligence is used in everyday devices, allowing AI tasks to be processed locally without relying on remote cloud servers, making them faster, more privacy-protected and more energy-efficient.

“This system is like a smart workspace where everything is within arm’s reach instead of having to go back and forth between desks and file cabinets,” said KAIST researchers Hakcheon Jeong and Seungjae Han, who led the development of this technology. “This is similar to the way our brain processes information, where everything is processed efficiently at once at one spot.”

The research findings have been published in the journal Nature Electronics.

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