Co-design for Kolmogorov-Arnold networks to unlock the full potential of optical intelligent accelerators.

The explosive growth in computational demands of artificial neural networks has spurred research into optical neural networks. However, most existing work overlooks the co-design of software and hardware, resulting in challenges with data encoding and nonlinear activation in optical neural networks, failing to fully leverage the potential of optical computing hardware. In this work, we propose a nonlinear optical processing unit (NL-OPU) based on the nonlinear response of Mach-Zehnder modulators (MZMs) for an optical Kolmogorov-Arnold network (OKAN), which bypasses the challenges related to linear data representation and nonlinear activation execution in optical neural networks.

Silicon photonic convolution operator exploiting on-chip nonlinear activation function.

Nonlinear activation functions (NAFs) are essential in artificial neural networks, enhancing learning capabilities by capturing complex input-output relationships. However, most NAF implementations rely on additional optoelectronic devices or digital computers, reducing the benefits of optical computing. To address this, we propose what we believe to be the first implementation of a nonlinear modulation process using an electro-optic IQ modulator on a silicon photonic convolution operator chip as a novel NAF.

Coherent optical neuron control based on reinforcement learning.

Optical neural networks take optical neurons as the cornerstone to achieve complex functions. The coherent optical neuron has become one of the mainstream implementations because it can effectively perform natural and even complex number calculations. However, its state variability and requirement for reliability and effectiveness render traditional control methods no longer applicable.