Thermal analog computing: Application to matrix-vector multiplication with inverse-designed metastructures
Author(s) •
Silva, Caio
Romano, Giuseppe
Date Issued
January 29, 2026
Journal
Physical Review Applied
Publisher
American Physical Society
Citation
Silva, Caio and Romano, Giuseppe. 2026. "Thermal analog computing: Application to matrix-vector multiplication with inverse-designed metastructures." Physical Review Applied, 25.
Version
Final published version
Abstract
The rising computational demand of modern workloads has renewed interest in energy-efficient paradigms, such as neuromorphic and analog computing. A fundamental operation in these systems is matrix-vector multiplication (MVM), ubiquitous in signal processing and machine learning. Here, we demonstrate MVM using inverse-designed metastructures that exploit heat conduction as the signal carrier. The proposed approach is based on a generalization of effective thermal conductivity to systems with multiple input and output ports: The input signal is encoded as a set of applied temperatures, while the output is represented by the power collected at designated terminals. The metastructures are obtained via density-based topology optimization, enabled by a differentiable thermal transport solver and automatic differentiation, achieving an accuracy greater than 99% in most cases across a pool of matrices with dimensions 2 ×2 and 3 ×3. We apply this methodology—termed thermal analog computing—to realize matrices relevant to practical tasks, including the discrete Fourier transform and convolutional filters. These findings open avenues for analog information processing in thermally active environments, including temperature-gradient sensing in microelectronics and thermal control systems.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies
Terms of Use
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Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.1103/5drp-hrx1