Photonic Tensor Core circuits have been widely explored as possible hardware accelerators for the next generation of Machine Learning applications, due to the large bandwidth, low latency, and energy saving that light has. Many architectures have been presented, especially exploiting photonic integrated circuits. However, most of the proposed solutions lack some features, such as integration, scalability, or energy saving. In this paper, we review the major achievements in recent years, showing how high integration can lead to better performance, but it could also limit the scalability of the overall system.
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