A team of engineers at The University of Texas at Dallas has developed a small-scale computer prototype that learns more like the human brain.<\/p>\n
The brain-inspired hardware can recognize patterns and make predictions using far fewer training computations than conventional AI systems.<\/p>\n
Dr. Joseph S. Friedman, associate professor of electrical and computer engineering and leader of the NeuroSpinCompute Laboratory, said the breakthrough could transform how machines learn.<\/p>\n
\u201cOur work shows a potential new path for building brain-inspired computers that can learn on their own,\u201d he said.<\/p>\n
\u201cSince neuromorphic computers do not need massive amounts of training computations, they could power smart devices without huge energy costs.\u201d<\/p>\n
Traditional computers separate memory from processing, forcing data to move constantly between the two.<\/p>\n
This design makes artificial intelligence systems energy-intensive and heavily reliant on vast labeled datasets. Training large models can cost hundreds of millions of dollars.<\/p>\n
Neuromorphic computing works differently. It takes inspiration from the brain, where neurons and synapses process and store information together. Synapses strengthen or weaken based on activity, allowing continuous learning.<\/p>\n
Friedman\u2019s work builds on a principle from neuropsychologist Donald Hebb, known as Hebb\u2019s law: neurons that fire together, wire together.<\/p>\n
\u201cThe principle that we use for a computer to learn on its own is that if one artificial neuron causes another artificial neuron to fire, the synapse connecting them becomes more conductive,\u201d Friedman said.<\/p>\n
Using magnetic tunnel junctions<\/p>\n
At the core of the prototype are magnetic tunnel junctions (MTJs), nanoscale devices made of two magnetic layers separated by an insulator.<\/p>\n
Electrons can pass through the barrier more easily when the magnetic layers align in the same direction and less easily when they do not.<\/p>\n
By linking MTJs in a network, the researchers created a system that adjusts its own connections as signals pass through it. <\/p>\n
Some pathways strengthen while others weaken, similar to how the brain\u2019s synapses evolve during learning.<\/p>\n
MTJs also offer reliable data storage because of their binary switching behavior, addressing a major limitation that has long hindered other neuromorphic approaches.<\/p>\n
Toward scalable brain-like machines<\/p>\n
Friedman\u2019s next goal is to scale up the prototype so it can handle more complex learning tasks. A larger neuromorphic system could process data in real time while consuming only a fraction of the power used by today\u2019s AI chips<\/a>.<\/p>\n Such efficiency could make it possible for phones, wearables, and other edge devices to run advanced AI models locally without relying on massive cloud servers<\/a>. <\/p>\n It could also ease the growing strain on global data centers, which currently consume vast amounts of energy for AI computation<\/a>.<\/p>\n \u201cIf successful, this technology could allow smart devices to think and adapt without needing to constantly connect to the cloud,\u201d Friedman said.<\/p>\n