来自光学无序的超向量：用于超维计算的可编程编码与光学推理
Hypervectors from Optical Disorder: Programmable Encoding and Optical Inference for Hyperdimensional Computing
AI总结：
本文利用散射介质的光学无序物理生成超维计算所需的高维随机超向量，实现单次光学编码与推理，解决内存-计算权衡问题。
AI中文摘要：
超维计算（HDC）是一种将信息表示为称为超向量（HVs）的高维伪随机向量的计算框架，能够通过简单的代数运算实现学习和推理。超向量的维度提供了计算能力和错误鲁棒性，但所需的高维随机性必须存储或重新生成，这带来了内存与计算之间的权衡。在此，我们通过将超向量生成所需的随机性物理地体现在散射介质的静态无序中来解决这一权衡。具体而言，一个硅光子电路与散射介质相结合，在单次光学拍摄中从少量输入值生成高维超向量。检测器侧的编码为连续和分类表示编程超向量的相关性。所得超向量再现了理想独立同分布随机超向量的关键统计和组合性质，包括成对相似性统计、联想记忆容量和分解容量。我们进一步演示了使用生成的超向量和光学相似性评估的光学辅助推理路径。
英文摘要：
Hyperdimensional computing (HDC) is a computing framework that represents information as high-dimensional pseudorandom vectors called hypervectors (HVs), enabling learning and inference through simple algebraic operations. The HV dimensionality provides computational capacity and error robustness, but the required high-dimensional randomness must be stored or regenerated, imposing a memory--computation tradeoff. Here, we address this tradeoff by physically embodying the randomness required for HV generation in the static disorder of a scattering medium. Specifically, a silicon photonic circuit combined with the scattering medium generates high-dimensional HVs from a small number of input values in a single optical shot. Detector-side encoding programs HV correlations for continuous and categorical representations. The resulting HVs reproduce key statistical and compositional properties of ideal i.i.d.\ random HVs, including pairwise similarity statistics, associative-memory capacity, and factorization capacity. We further demonstrate an optically assisted inference path using the generated HVs and optical similarity evaluation.
