Reprogrammable, in-materia matrix-vector multiplication with floppy modes

TL;DR

This paper demonstrates a reprogrammable elastic metamaterial that performs matrix-vector multiplications using floppy modes, enabling soft-matter intelligent systems with reconfigurable computational capabilities.

Contribution

The authors introduce a novel, reprogrammable elastic metamaterial design utilizing floppy modes for matrix-vector multiplication, overcoming topological challenges in soft-matter systems.

Findings

Successfully demonstrated reprogrammable matrix-vector multiplication in elastic metamaterials

Floppy modes enable complex deformations without high energy costs

Potential applications in embodied intelligence and in-sensor computing

Abstract

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.