MMTs edge closer to artificial intelligence networks

According to the university, the advance marks a key step towards using thin-film transistors as artificial intelligence hardware and moves edge computing forward, with the prospect of reducing power needs and improving efficiency, rather than relying solely on computer chips.

The MMT, first reported by Surrey researchers in 2020, is said to overcome long-standing challenges associated with transistors and can perform the same operations as more complex circuits. This latest research, published in Scientific Reports, uses mathematical modelling to prove the concept of using MMTs in artificial intelligence systems.

Using measured and simulated transistor data, the researchers show that well-designed multimodal transistors could operate robustly as rectified linear unit-type (ReLU) activations in artificial neural networks, achieving practically identical classification accuracy as pure ReLU implementations.

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They used measured and simulated MMT data to train an artificial neural network to identify handwritten numbers and compared the results with the built-in ReLU of the software. The results confirmed the potential of MMT devices for thin-film decision and classification circuits. The same approach could be used in more complex AI systems.