Fiber optic computing using distributed feedback

TL;DR

This paper presents a fiber-optic computing architecture that uses temporal multiplexing and distributed feedback to perform multiple convolutions simultaneously, enabling efficient, low-power, high-throughput analog computing suitable for machine learning tasks.

Contribution

The authors introduce a novel fiber-optic computing method based on temporal encoding and Rayleigh backscattering, allowing multiple kernel convolutions in a single layer with passive fiber components.

Findings

Performs multiple convolutions using fiber-based temporal multiplexing.

Achieves lower power consumption compared to GPU-based systems.

Integrates seamlessly with fiber-optic communication for remote sensing applications.

Abstract

The widespread adoption of machine learning and other matrix intensive computing algorithms has inspired renewed interest in analog optical computing, which has the potential to perform large-scale matrix multiplications with superior energy scaling and lower latency than digital electronics. However, most existing optical techniques rely on spatial multiplexing to encode and process data in parallel, requiring a large number of high-speed modulators and detectors. More importantly, most of these architectures are restricted to performing a single kernel convolution operation per layer. Here, we introduce a fiber-optic computing architecture based on temporal multiplexing and distributed feedback that performs multiple convolutions on the input data in a single layer (i.e. grouped convolutions). Our approach relies on temporally encoding the input data as an optical pulse train and injecting it into an optical fiber where partial reflectors create a series of delayed copies of the input vector. In this work, we used Rayleigh backscattering in standard single mode fiber as the partial reflectors to encode a series of random kernel transforms. We show that this technique effectively performs a random non-linear projection of the input data into a higher dimensional space which can facilitate a variety of computing tasks, including non-linear principal component analysis, support vector machines, or extreme learning machines. By using a passive fiber to perform the kernel transforms, this approach enables efficient energy scaling with orders of magnitude lower power consumption than GPUs, while using a high-speed modulator and detector maintains low latency and high data-throughput. Finally, our approach is readily integrated with fiber-optic communication links, enabling additional applications such as processing remote sensing data transmitted in the analog domain.