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A bendable AI chip for wearable technology

Co-optimized design and technology has resulted in cheap, flexible microchips that efficiently run neural-network tasks, as demonstrated in wearable health-care devices.
By
  1. Kris Myny

    1. Kris Myny is in the Department of Electrical Engineering, KU Leuven, 3001 Leuven, Belgium.

  2. Djihad Nacereddine Bouakaz

    1. Djihad Nacereddine Bouakaz is in the Department of Electrical Engineering, KU Leuven, 3001 Leuven, Belgium.

Machine learning and artificial intelligence are currently transforming society, but they demand an enormous amount of computing power. At the opposite end of the computer-power spectrum are extreme-edge devices, which are made using ultra-low-cost electronic components. Such devices execute basic computations locally before sending compressed data to the cloud and often need to be installed on flexible substrates — think of wearable sensors that process biometric data, brain implants for recording neural activity or applications in the ‘Internet of Things’. Writing in Nature, Yan et al.1 report the implementation of AI chips directly on flexible substrates, paving the way to ultra-low-cost AI systems for use in extreme-edge technology.

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Nature 649, 1114-1115 (2026)

doi: https://doi.org/10.1038/d41586-026-00037-6

References

  1. Yan, A. et al. Nature 649, 1165–1171 (2026).

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  2. Yan, A. et al. Adv. Func. Mater. 34, 2304409 (2024).

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  3. Ozer, E. et al. Nature 634, 341–346 (2024).

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  4. Çeliker, H., Dehaene, W. & Myny, K. Nature 629, 335–340 (2024).

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  5. Verma, N. et al. IEEE Solid-State-Circuits Mag. 11, 43–55 (2019).

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  6. Geng, D. et al. Nature Electron. 6, 963–972 (2023).

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Competing Interests

The authors declare no competing interests.

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