Increasing AI’s ability to tackle complex challenges with greater accuracy and energy efficiency is not simply a matter of adding more computing power. Subtle details in the networking of the computing elements can have a significant impact on AI performance.

The architecture, or wiring, of the computing elements in an AI is inspired by how neurons form circuits to process information and learn. However, a key aspect of neural network structure has so far been overlooked in AI design, as a KAUST-led team has shown.

Jesper Tegnér and his team at KAUST –  in collaboration with an international team including the AI technology company NVIDIA –  made the discovery while examining the network architecture of an AI solving a balance task. “We focused on ‘network motifs’, which often form the fundamental building blocks of large, complex networks,” explains Haoling Zhang, a Ph.D. student in Tegnér’s lab.

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