Abstract
Mitigating the energy requirements of artificial intelligence requires novel physical substrates for computation. Phononic metamaterials have vanishingly low power dissipation and hence are a prime candidate for green, always-on computers. However, their use in machine learning applications has not been explored due to the complexity of their design process. Current phononic metamaterials are restricted to simple geometries (e.g., periodic and tapered) and hence do not possess sufficient expressivity to encode machine learning tasks. A non-periodic phononic metamaterial, directly from data samples, that can distinguish between pairs of spoken words in the presence of a simple readout nonlinearity is designed and fabricated, hence demonstrating that phononic metamaterials are a viable avenue towards zero-power smart devices. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000651589Publication status
publishedExternal links
Journal / series
Advanced Functional MaterialsVolume
Pages / Article No.
Publisher
Wiley-VCHOrganisational unit
08714 - Gruppe Huber
03953 - Robertsson, Johan / Robertsson, Johan
Funding
694407 - MAchine for Time Reversal and Imersive wave eXperiments (EC)
771503 - Topological Mechanical Metamaterials (EC)
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