Fig. 1: Concept of our integrated photonic tensor core (IPTC).
From: 120 GOPS Photonic tensor core in thin-film lithium niobate for inference and in situ training
a Top: Applications and functions of artificial intelligence (AI)44,54,55,56. AI systems require processors to be adaptable to analyze data from different devices for various AI tasks, including supervised and unsupervised learning AI tasks. Bottom: A schematic of our proposed IPTC, consisting of four physical components: lasers, two thin-film lithium niobate (TFLN) Mach-Zehnder modulators (MZMs), and charge-integration photoreceivers. Using these four physical components, our processor can implement an entire layer of a neural network. b A schematic of a conventional wavelength-division multiplexing (WDM)-based IPTC, which includes m neurons. PCM: phase change material. c The performance of our device compares with that of several state-of-the-art photonic tensor cores9,10,11,15,18,28,30 in terms of compactness, dot product operation principle, computational speed, and the available dimension of vector in a dot product. Here, the available dimension means the processor completely executes the dot product operation without the assistance of traditional digital electronic computers.
