Extending Energy-Efficient and Scalable DNN Training and Inference With 3-D Photonic Accelerator

<p dir="ltr">As deep neural network (DNN) models continue to grow in complexity, analog computing architectures have emerged as a promising solution to meet increasing computational demands. Among these, silicon photonic computing excels at efficiently executing dot product operations while leveraging inherent parallelism. Photonic phase change memory (photonic-PCM) further enhances photonic computing by enabling scalable, non-volatile storage. In this work, we introduce the 3D Large-Scale Photonic Accelerator (LSPA), a novel photonic computing architecture designed for large-scale DNN models.</p>