Photonic neuromorphic computing is raising a growing interest as it promises to provide massive parallelism and low power consumption. In this paper, we demonstrate for the first time a feed-forward neural network via an 8x8 Indium Phosphide cross-connect chip, where up to 8 on-chip weighted addition circuits are co-integrated, based on semiconductor optical amplifier technology. We perform the weight calibration per neuron, resulting in a normalized root mean square error smaller than 0.08 and a best case dynamic range of 27 dB. The 4:1 weighted addition operation is executed on-chip and is part of a neuron whose non-linear function is implemented via software. A three feedback loop optimization procedure is demonstrated to enable an output neuron accuracy improvement of up to 55%. The exploitation of this technology as the neural network is evaluated by implementing a trained 3-layer photonic deep neural network to solve the Iris flower classification problem. Prediction accuracy of 85.8% is achieved, with respect to the 95% accuracy obtained via a computer. A comprehensive analysis of the error evolution in our system reveals that the electrical/optical conversions dominate the error contribution, which suggests that an all optical approach is preferable for future neuromorphic computing hardware design.

We present what is to our knowledge the first active-passive monolithically integrated $16\times 16$ switch. The active InP/InGaAsP elements provide semiconductor optical amplifier gates in a multistage rearrangeably nonblocking switch design. Thirty-two representative connections, including the shortest, longest, and comprehensive range of intermediate paths have been assessed across the switch circuit. The $10\mathrm{Gb}/\mathrm{s}$ signal routing is demonstrated with an optical signal-to-noise ratio up to $28.3\mathrm{dB}/0.1\mathrm{nm}$ and a signal extinction ratio exceeding 50 dB.

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