TY - JOUR A2 - Wang, Xiaojie AU - Li, Mengkun AU - Wang, Yongjian PY - 2020 DA - 2020/12/16 TI - An Energy-Efficient Silicon Photonic-Assisted Deep Learning Accelerator for Big Data SP - 6661022 VL - 2020 AB - 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. SN - 1530-8669 UR - https://doi.org/10.1155/2020/6661022 DO - 10.1155/2020/6661022 JF - Wireless Communications and Mobile Computing PB - Hindawi KW - ER -