Neural Networks Advance with Fast, Low-Energy Matrix-Vector Multiplication via Brillouin Scattering
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The demand for energy-efficient computing drives intense research into novel approaches to artificial intelligence, and analog coprocessors represent a promising pathway. I. V. Vovchenko, A. A. Zyablovsky, A. A. Pukhov, and E. S. Andrianov demonstrate a new optical coprocessor that performs complex calculations using the principles of spontaneous Brillouin scattering. Their system leverages ring resonators and their interaction with thermal vibrations to achieve rapid, parallel matrix-vector multiplication, a fundamental operation in many machine learning algorithms. This innovative approach offers the potential for significantly reducing the energy consumption of artificial intelligence systems while maintaining high processing speeds, representing a substantial advance in the field of neuromorphic computing. Ring resonators are an intensively developing field, providing approximate results of computations for relatively low energy cost and at high speed. This work demonstrates that a set of ring resonators, with Brillouin interaction between photons and phonons and coupled to a waveguide, can implement matrix-vector multiplication. The input vector forms from the occupancies of the anti-Stokes optical modes pumped via spontaneous Brillouin scattering, which occurs on thermal phonons. Brillouin scattering rates and coupling constants between the ring resonators and the waveguide constitute the matrix, enabling parallel computations in the frequency band.
Master Equation Derivation for Brillouin Scattering This appendix provides the mathematical foundations for the research, detailing the derivations and justifications for the models and equations used. It details the derivation of the master equation, which describes how optical modes and phonons interact within the microresonator, and solves this equation to determine the equilibrium occupancies of the optical modes and the anti-Stokes mode. The derivation begins by defining variables representing the average values of operators related to the optical modes and their interactions, establishing a steady-state condition, and solving a system of algebraic equations to find equilibrium occupancies. The detailed mathematical derivations employ techniques such as integration by parts and the Markov approximation to simplify complex equations and accurately model the interactions between light and matter within the microresonator. Understanding these derivations is essential for verifying the validity of the research and for optimizing microresonator designs and controlling light-matter interactions at the nanoscale.
Brillouin Ring Resonators Perform Matrix Multiplication Scientists have demonstrated a novel approach to analog computation using ring resonators and the principles of Brillouin interaction between light and sound waves, potentially offering a faster and more energy-efficient alternative to traditional digital processors. The research team successfully designed a system where a network of ring resonators, coupled to an optical waveguide, performs matrix-vector multiplication, a fundamental operation in neural networks and other computational tasks. This system leverages the excitation of anti-Stokes optical modes within the ring resonators, driven by spontaneous Brillouin scattering from thermal phonons, to encode and process information. The core of the system involves coupling the optical modes of the ring resonators to both the waveguide and internal reservoirs of thermal phonons. Experiments reveal that the system enables parallel computations across a frequency band, significantly reducing processing time, and exhibits weak dependence on the input vector’s dimension. The researchers demonstrated that the system’s design allows for the replication of this process, enabling the implementation of complex matrix operations, and delivers a promising pathway towards energy-efficient and high-performance analog coprocessors.
Photonic Resonators Perform Fast Matrix Multiplication This work demonstrates a novel approach to analog computation using integrated photonics. Researchers have shown that a system of ring resonators, interacting with both light and sound waves (phonons), can perform matrix-vector multiplication, a fundamental operation in neural networks. The system leverages spontaneous Brillouin scattering to encode input vectors and utilizes the coupling between resonators and a waveguide to execute the computation. Importantly, the output of the system, the intensity of light in the waveguide, is directly proportional to the scalar product of the input vector and a matrix defined by the resonator properties. The achievement enables parallel computation across multiple resonator modes, potentially increasing processing speed by a factor of one hundred to ten thousand, with computational time estimated to be on the nanosecond scale and largely independent of the input vector’s size. The researchers highlight the potential for significant energy savings and increased speed by integrating both linear and non-linear operations onto a single photonic platform. 👉 More information 🗞 Optical coprocessor based on spontaneous Brillouin scattering 🧠 ArXiv: https://arxiv.org/abs/2512.15970 Tags: