Neural networks, having achieved breakthroughs in many applications, require extensive convolutions and matrix-vector multiplication operations. To accelerate these operations, benefiting from power efficiency, low latency, large bandwidth, massive parallelism, and CMOS compatibility, silicon photonic neural networks have been proposed as a promising solution. In this study, we propose a scalable architecture based on a silicon photonic integrated circuit and optical frequency combs to offer high computing speed and power efficiency. A proof-of-concept silicon photonics neuromorphic accelerator based on integrated coherent transmit–receive optical sub-assemblies, operating over 1TOPS with only one computing cell, is experimentally demonstrated. We apply it to process fully connected and convolutional neural networks, achieving a competitive inference accuracy of up to 96.67% in handwritten digit recognition compared to its electronic counterpart. By leveraging optical frequency combs, the approach’s computing speed is possibly scalable with the square of the cell number to realize over 1 Peta-Op/s. This scalability opens possibilities for applications such as autonomous vehicles, real-time video processing, and other high-performance computing tasks.

中文翻译：

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使用集成相干发射-接收光学子组件的硅光子神经形态加速器

神经网络在许多应用中取得了突破，需要大量的卷积和矩阵向量乘法运算。为了加速这些操作，受益于功效、低延迟、大带宽、大规模并行性和 CMOS 兼容性，硅光子神经网络已被提出作为一种有前景的解决方案。在这项研究中，我们提出了一种基于硅光子集成电路和光学频率梳的可扩展架构，以提供高计算速度和功率效率。实验证明了一种基于集成相干发射-接收光学子组件的概念验证硅光子神经形态加速器，仅用一个计算单元即可运行超过 1TOPS。我们将其应用于处理全连接和卷积神经网络，与电子对应物相比，手写数字识别的推理准确率高达 96.67%。通过利用光学频率梳，该方法的计算速度可能会随着细胞数量的平方而扩展，以实现超过 1 Peta-Op/s。这种可扩展性为自动驾驶汽车、实时视频处理和其他高性能计算任务等应用提供了可能性。