Reprogrammable, in-materia matrix-vector multiplication with floppy modes
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912052138737664 |
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| author | Louvet, Theophile Omidvar, Parisa Serra-Garcia, Marc |
| author_facet | Louvet, Theophile Omidvar, Parisa Serra-Garcia, Marc |
| contents | Matrix-vector multiplications are a fundamental building block of artificial intelligence; this essential role has motivated their implementation in a variety of physical substrates, from memristor crossbar arrays to photonic integrated circuits. Yet their realization in soft-matter intelligent systems remains elusive. Here, we experimentally demonstrate a reprogrammable elastic metamaterial that computes matrix-vector multiplications using floppy modes -- deformations with near-zero stored elastic energy. Floppy modes allow us to program complex deformations without being hindered by the natural stiffness of the material; but their practical application is challenging, as their existence depends on global topological properties of the system. To overcome this challenge, we introduce a continuously parameterized unit cell design with well-defined compatibility characteristics. This unit cell is then combined to form arbitrary matrix-vector multiplications that can even be reprogrammed after fabrication. Our results demonstrate that floppy modes can act as key enablers for embodied intelligence, smart MEMS devices and in-sensor edge computing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_20425 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reprogrammable, in-materia matrix-vector multiplication with floppy modes Louvet, Theophile Omidvar, Parisa Serra-Garcia, Marc Soft Condensed Matter Emerging Technologies Matrix-vector multiplications are a fundamental building block of artificial intelligence; this essential role has motivated their implementation in a variety of physical substrates, from memristor crossbar arrays to photonic integrated circuits. Yet their realization in soft-matter intelligent systems remains elusive. Here, we experimentally demonstrate a reprogrammable elastic metamaterial that computes matrix-vector multiplications using floppy modes -- deformations with near-zero stored elastic energy. Floppy modes allow us to program complex deformations without being hindered by the natural stiffness of the material; but their practical application is challenging, as their existence depends on global topological properties of the system. To overcome this challenge, we introduce a continuously parameterized unit cell design with well-defined compatibility characteristics. This unit cell is then combined to form arbitrary matrix-vector multiplications that can even be reprogrammed after fabrication. Our results demonstrate that floppy modes can act as key enablers for embodied intelligence, smart MEMS devices and in-sensor edge computing. |
| title | Reprogrammable, in-materia matrix-vector multiplication with floppy modes |
| topic | Soft Condensed Matter Emerging Technologies |
| url | https://arxiv.org/abs/2409.20425 |