Neuromorphic Computing via Fission-based Broadband Frequency Generation

The performance limitations of traditional computer architectures have led to the rise of brain-inspired hardware, with optical solutions gaining popularity due to the energy efficiency, high speed, and scalability of linear operations. However, the use of optics to emulate the synaptic activity of neurons has remained a challenge since the integration of nonlinear nodes is power-hungry and, thus, hard to scale. Neuromorphic wave computing offers a new paradigm for energy-efficient information processing, building upon transient and passively nonlinear interactions between optical modes in a waveguide. Here, an implementation of this concept is presented using broadband frequency conversion by coherent higher-order soliton fission in a single-mode fiber. It is shown that phase encoding on femtosecond pulses at the input, alongside frequency selection and weighting at the system output, makes transient spectro-temporal system states interpretable and allows for the energy-efficient emulation of various digital neural networks. The experiments in a compact, fully fiber-integrated setup substantiate an anticipated enhancement in computational performance with increasing system nonlinearity. The findings suggest that broadband frequency generation, accessible on-chip and in-fiber with off-the-shelf components, may challenge the traditional approach to node-based brain-inspired hardware design, ultimately leading to energy-efficient, scalable, and dependable computing with minimal optical hardware requirements. This work presents an all-fiber optical approach that harnesses the capabilities of nonlinear wave dynamics to process information. The study suggests that broadband frequency mixing in a single optical waveguide can mimic various digital neural networks, opening up a non-intuitive use case for nonlinear photonics as a scalable resource for energy-efficient brain-inspired computing.image

Automated summary

This study presents an implementation of neuromorphic wave computing using broadband frequency conversion in a single-mode fiber. The results show that through phase encoding and frequency selection and weighting, energy-efficient emulation of various digital neural networks is possible. The experiments demonstrate an enhancement in computational performance with increasing system nonlinearity. The findings also suggest that broadband frequency generation accessible on-chip and in-fiber may challenge traditional approaches to brain-inspired hardware design, leading to energy-efficient and scalable computing.