Hardware Implementation of Photonic Spiking Hash Retrieval

TL;DR

This paper introduces a novel photonic spiking hashing system that leverages nonlinear laser dynamics and hardware-aware training to achieve ultra-fast, energy-efficient similarity search with robust binary code generation.

Contribution

It presents a hardware-software co-designed photonic hashing framework using DFB-SA lasers and a new quantization loss to improve robustness against optical noise.

Findings

Achieves 2.294 ns/query encoding latency.

Consumes only 73.70 pJ/query energy.

Maintains high retrieval accuracy comparable to digital methods.

Abstract

Hashing retrieval is a pivotal technology for large-scale similarity search, widely applied in retrieval-augmented generation (RAG) for large language models (LLMs), massive image repositories, and bioinformatics sequence matching. However, traditional electronic hashing implementations face severe bottlenecks in power consumption and latency when processing high-dimensional data, while existing photonic neural networks often lack robust mechanisms for direct binary code generation under analog noise. To address these challenges, we propose a hardware-software co-designed photonic spiking hashing framework. We utilize the nonlinear thresholding dynamics of a distributed feedback laser with saturable absorber (DFB-SA) to realize the final binarization of a single-step spiking neural network (SNN). Crucially, a hardware-aware quantization margin loss is introduced to maximize the decision margin, effectively mitigating bit flips caused by optical intensity fluctuations. Validated on MNIST (image) and 20 Newsgroups (text) datasets, our system demonstrates robust binary code generation and high retrieval accuracy comparable to digital baselines. Most significantly, the proposed photonic architecture exhibits superior efficiency with an encoding latency of 2.294 ns/query and an energy consumption of 73.70 pJ/query. This work offers a robust and viable path for ultra-fast, energy-efficient optoelectronic neuromorphic computing in high-throughput information retrieval tasks.