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Photonics offers a transformative approach to artificial intelligence (AI)and neuromorphic computing by enabling low-latency, high-speed, andenergy-efficient computations. However, conventional photonic tensor cores facesignificant challenges in constructing large-scale photonic neuromorphicnetworks. Here, we propose a fully integrated photonic tensor core, consistingof only two thin-film lithium niobate (TFLN) modulators, a III-V laser, and acharge-integration photoreceiver. Despite its simple architecture, it iscapable of implementing an entire layer of a neural network with acomputational speed of 120 GOPS, while also allowing flexible adjustment of thenumber of inputs (fan-in) and outputs (fan-out). Our tensor core supports rapidin-situ training with a weight update speed of 60 GHz. Furthermore, itsuccessfully classifies (supervised learning) and clusters (unsupervisedlearning) 112 * 112-pixel images through in-situ training. To enable in-situtraining for clustering AI tasks, we offer a solution for performingmultiplications between two negative numbers.

120 GOPS Photonic Tensor Core in Thin-film Lithium Niobate for Inference and in-situ Training