Deep learning has become the most mainstream technology in artificial intelligence (AI) because it can be comparable to human performance in complex tasks. However, in the era of big data, the ever-increasing data volume and model scale makes deep learning require mighty computing power and acceptable energy costs. For electrical chips, including most deep learning accelerators, transistor performance limitations make it challenging to meet computing’s energy efficiency requirements. Silicon photonic devices are expected to replace transistors and become the mainstream components in computing architecture due to their advantages, such as low energy consumption, large bandwidth, and high speed. Therefore, we propose a silicon photonic-assisted deep learning accelerator for big data. The accelerator uses microring resonators (MRs) to form a photonic multiplication array. It combines photonic-specific wavelength division multiplexing (WDM) technology to achieve multiple parallel calculations of input feature maps and convolution kernels at the speed of light, providing the promise of energy efficiency and calculation speed improvement. The proposed accelerator achieves at least a 75x improvement in computational efficiency compared to the traditional electrical design.

中文翻译：

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节能的硅光子辅助深度学习加速器，用于大数据

深度学习已成为人工智能（AI）中最主流的技术，因为它可以与人类在复杂任务中的表现相提并论。但是，在大数据时代，不断增长的数据量和模型规模使深度学习需要强大的计算能力和可接受的能源成本。对于包括大多数深度学习加速器在内的电子芯片，晶体管性能的局限性使其难以满足计算的能效要求。硅光子器件因其低能耗，大带宽和高速等优点而有望取代晶体管，并成为计算架构中的主流组件。因此，我们提出了一种用于大数据的硅光子辅助深度学习加速器。加速器使用微环谐振器（MR）形成光子倍增阵列。它结合了特定于光子的波分复用（WDM）技术，以光速实现输入特征图和卷积核的多次并行计算，从而有望提高能源效率并提高计算速度。与传统的电气设计相比，建议的加速器在计算效率上至少提高了75倍。