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Photonic Neural Networks (PNNs) implemented on silicon photonic (SiPho) platforms stand out as a promising candidate to endow neural network hardware, offering the potential for energy efficient and ultra-fast computations through exploiting the unique primitives of light i.e., THz bandwidth, low-power and low-latency. In this paper, we review the state-of-the-art photonic linear processors discuss their challenges and propose solutions for future photonic-assisted machine learning engines. Additionally, we will present experimental results on the recently introduced SiPho 4x4 coherent crossbar (Xbar) architecture, that migrates from existing Singular Value Decomposition (SVD)-based schemes while offering single time-step programming complexity. The Xbar architecture utilizes silicon germanium (SiGe) Electro-Absorption Modulators (EAMs) as its computing cells and Thermo-Optic (TO) Phase Shifters (PS) for providing the sign information at every weight matrix node. Towards experimentally evaluating our Xbar architecture, we performed 10,024 arbitrary linear transformations over the SiPho processor, with the respective fidelity values converging to 100%. Followingly, we focus on the execution of the non-linear part of the NN by demonstrating a programmable analog optoelectronic circuit that can be configured to provide a plethora of non-linear activation functions, including tanh, sigmoid, ReLU and inverted ReLU at 2 GHz update rate. Finally, we provide a holistic overview on optics-informed neural networks towards improving the classification accuracy and performance of optics-specific Deep Learning (DL) computational tasks by leveraging the synergy of optical physics and DL.
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