All-Optical High-speed Programmable Nonlinear Activation Functions using a Fabry-Perot Laser

2025-03-27

AI Summary

Photonic neural networks urgently require all-optical nonlinear activation functions that simultaneously achieve high speed, programmability, and energy efficiency—yet existing approaches face fundamental trade-offs among bandwidth, energy consumption, and reconfigurability. This work introduces an all-optical activation function based on a Fabry–Perot laser diode, enabling dynamic, continuous-wave optical injection to reconfigure the nonlinear transfer characteristic (e.g., sigmoid, inverted PReLU) in real time. The device supports ultrashort 25-ps optical pulses at up to 10 GHz repetition rate, operates without electronic control, exhibits negligible static power dissipation, and achieves single-operation energy consumption as low as ~100 fJ. Through ultrafast optical characterization and hardware validation in a photonic neural network driven by real random data, we demonstrate concurrent breakthroughs in energy efficiency, operational speed, and functional programmability—establishing a critical enabler for scalable, high-performance photonic AI accelerators.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The threads of photonics are eagerly awaited to redefine the future of neuromorphic data processing, especially as the computing-intensive artificial intelligence models become an unavoidable part of our everyday lives. Still, there is much to be improved within the domain of photonic nonlinear activation functions, as the programmable, all-optical, energy-efficient nonlinearities remain beyond the grasp of today's state of the art. In this paper, we address the issue at hand and propose a novel approach in the realization of high-performing all-optical photonic activations. Through simulations and experiments, we show that Fabry-Perot laser diodes (FP-LDs) exhibit richness and high programmability of their nonlinear response to input optical pulses with widths as low as 25 ps. We demonstrate a variety of sigmoid-like and inverted PReLU-like trends to be used as all-optical activation functions in photonic neural networks, testing their performance in stringent, real-life training scenarios with randomized data patterns at repetition rates up to 10 GHz. The programmability of activations is shown using a multitude of experimental operating parameters, among which we highlight the power variation of an additional continuous wave laser, injected into the FP-LD, enriching our approach with all-optical control of all-optical activations. With very low static power consumption of our active element, we achieve a record-breaking energy draw on the order of pJ to hundreds of fJ per nonlinear operation.

Problem

• Develop programmable all-optical nonlinear activation functions
• Achieve energy-efficient photonic neural network operations
• Demonstrate high-speed performance with Fabry-Perot lasers

Innovation

• Fabry-Perot laser diodes for all-optical activations
• Programmable nonlinear response to optical pulses
• Ultra-low energy consumption per nonlinear operation