Deep learning with coherent VCSEL neural networks

Energy consumption and compute density are challenges for computing systems. Here researchers show an optical computing architecture using micrometre-scale VCSEL transmitter arrays enabling 7 fJ energy per operation and a potential compute density of 6 tera-operations mm(-2) s(-1). Deep neural networks (DNNs) are reshaping the field of information processing. With the exponential growth of these DNNs challenging existing computing hardware, optical neural networks (ONNs) have recently emerged to process DNN tasks with high clock rates, parallelism and low-loss data transmission. However, existing challenges for ONNs are high energy consumption due to their low electro-optic conversion efficiency, low compute density due to large device footprints and channel crosstalk, and long latency due to the lack of inline nonlinearity. Here we experimentally demonstrate a spatial-temporal-multiplexed ONN system that simultaneously overcomes all these challenges. We exploit neuron encoding with volume-manufactured micrometre-scale vertical-cavity surface-emitting laser (VCSEL) arrays that exhibit efficient electro-optic conversion (V-& pi; = 4 mV) and compact footprint (<0.01 mm(2) per device). Homodyne photoelectric multiplication allows matrix operations at the quantum-noise limit and detection-based optical nonlinearity with instantaneous response. With three-dimensional neural connectivity, our system can reach an energy efficiency of 7 femtojoules per operation (OP) with a compute density of 6 teraOP mm(-)(2) s(-1), representing 100-fold and 20-fold improvements, respectively, over state-of-the-art digital processors. Near-term development could improve these metrics by two more orders of magnitude. Our optoelectronic processor opens new avenues to accelerate machine learning tasks from data centres to decentralized devices.

Automated summary

Researchers demonstrate an optical computing architecture using micrometre-scale VCSEL transmitter arrays, achieving an energy efficiency of 7 fJ per operation and a compute density of 6 tera-operations mm(-2) s(-1). This system overcomes the challenges of ONNs, such as high energy consumption, low compute density, and long latency, providing a new way to accelerate machine learning tasks.