The rapid rise of machine learning drives demand for extensive matrix-vector multiplication operations, thereby challenging the capacities of traditional von Neumann computing systems. Researchers explore alternatives, such as in-memory computing architecture, to find energy-efficient solutions. In particular, there is renewed interest in optical computing systems, which could potentially handle matrix-vector multiplication in a more energy-efficient way. Despite promising initial results, developing high-throughput optical computing systems to rival electronic hardware remains a challenge. Here, we propose and demonstrate a hyperspectral in-memory computing architecture, which simultaneously utilizes space and frequency multiplexing, using optical frequency combs and programmable optical memories. Our carefully designed three-dimensional opto-electronic computing system offers remarkable parallelism, programmability, and scalability, overcoming typical limitations of optical computing. We have experimentally demonstrated highly parallel, single-shot multiply-accumulate operations with precision exceeding 4 bits in both matrix-vector and matrix-matrix multiplications, suggesting the system’s potential for a wide variety of deep learning and optimization tasks. Our approach presents a realistic pathway to scale beyond peta operations per second, a major stride towards high-throughput, energy-efficient optical computing.

中文翻译：

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使用光学频率梳和可编程光学存储器的高光谱内存计算


机器学习的快速兴起推动了对广泛矩阵向量乘法运算的需求，从而挑战了传统冯·诺依曼计算系统的能力。研究人员探索内存计算架构等替代方案，以寻找节能解决方案。特别是，人们对光学计算系统重新产生了兴趣，它有可能以更节能的方式处理矩阵向量乘法。尽管初步结果令人鼓舞，但开发高通量光学计算系统来与电子硬件竞争仍然是一个挑战。在这里，我们提出并演示了一种高光谱内存计算架构，该架构使用光学频率梳和可编程光学存储器同时利用空间和频率复用。我们精心设计的三维光电计算系统提供了卓越的并行性、可编程性和可扩展性，克服了光学计算的典型局限性。我们通过实验证明了在矩阵-向量和矩阵-矩阵乘法中精度超过 4 位的高度并行、单次乘法累加运算，表明该系统具有执行各种深度学习和优化任务的潜力。我们的方法提供了一条超越每秒 peta 操作的现实途径，这是向高吞吐量、节能光学计算迈出的一大步。