The rapid advancements in machine learning across numerous industries have amplified the demand for extensive matrix-vector multiplication operations, thereby challenging the capacities of traditional von Neumann computing architectures. To address this, researchers are currently exploring alternatives such as in-memory computing systems to develop faster and more energy-efficient hardware. In particular, there is renewed interest in computing systems based on optics, which could potentially handle matrix-vector multiplication in a more energy-efficient way. Despite promising initial results, developing a highly parallel, programmable, and scalable optical computing system capable of rivaling electronic computing hardware still remains elusive. In this context, we propose a hyperspectral in-memory computing architecture that integrates space multiplexing with frequency multiplexing of optical frequency combs and uses spatial light modulators as a programmable optical memory, thereby boosting the computational throughput and the energy efficiency. We have experimentally demonstrated multiply-accumulate operations with higher than 4-bit precision in both matrix-vector and matrix-matrix multiplications, which suggests the system's potential for a wide variety of deep learning and optimization tasks. This system exhibits extraordinary modularity, scalability, and programmability, effectively transcending the traditional limitations of optics-based computing architectures. Our approach demonstrates the potential to scale beyond peta operations per second, marking a significant step towards achieving high-throughput energy-efficient optical computing.

翻译：机器学习在众多行业的快速进步加剧了对大规模矩阵向量乘法运算的需求，从而对传统冯·诺依曼计算架构的能力提出了挑战。为解决这一问题，研究人员正在探索内存计算系统等替代方案，以开发更快、更节能的硬件。特别地，基于光学的计算系统重新引起关注，这类系统有望以更高能效方式处理矩阵向量乘法。尽管初步成果令人鼓舞，但开发能够与电子计算硬件相抗衡的高度并行化、可编程且可扩展的光学计算系统仍面临挑战。在此背景下，我们提出一种超光谱内存计算架构，该架构将光学频率梳的空分复用与频分复用相结合，并采用空间光调制器作为可编程光存储，从而提升计算吞吐量和能效。我们通过实验验证了在矩阵向量和矩阵矩阵乘法中实现精度高于4位的乘累加运算，表明该系统在各类深度学习与优化任务中具有应用潜力。该系统展现出卓越的模块化、可扩展性和可编程性，有效突破了传统光学计算架构的局限性。本方法展示了超越每秒千万亿次运算的扩展潜力，标志着向实现高吞吐量、高能效光学计算迈出了关键一步。