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

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. LSPA employs multi-layered 3D stacking of non-volatile photonic-PCM cells, creating a high-density computational fabric that optimizes energy efficiency, flexibility, and scalability. LSPAs custom 3D photonic network enables simultaneous data multicast in two dimensions and accumulation in three dimensions, optimizing communication patterns essential for efficient DNN training. A distinctive feature of LSPA is its ability to execute multiple forward and backward passes in parallel within each mini-batch, reducing latency associated with data movement and photonic-PCM programming. This unique capability combined with high-bandwidth photonic interconnects allows LSPA to sustain efficient training across a wide range of DNN workloads. When evaluated against a range of neural network models including VGG-16, ResNet-50, GoogLeNet, Transformer, GNMT, LLaMA 7B, and LLaMA 30B, LSPA reduces execution time by up to 92% and energy consumption by up to 90%. These results highlight LSPA as a transformative advancement in scalable, high-performance photonic computing for deep learning.