| 2014 | VANDOORNE K，Nature Communications［81］ | The integrated passive silicon photonics reservoir chip on a silicon platform | A 16-node square mesh reservoir.The chip contained waveguides，splitters，and combiners | Performed arbitrary Boolean logic operations with memory，5-bit header recognition up to 12.5 Gbit/s，and classification of spoken digits |
| 2017 | SHEN Yichen，Nature Photonics［70］ | Feedforward fully connected optical neural network based on MZI | 4×4 weight matrix based on56 MZIsThe neuron was simulated by a nonlinear activation function in the electrical domain | The accuracy of vowel recognition is 76.7%，which is more than two orders of magnitude faster than the latest electronic chip at that time，but the energy used is less than one thousandth |
| 2017 | TAIT A N，Scientific Reports［59］ | Recurrent silicon photonic neural network based on MRR | 4×4 weight matrix based on 16 MRRs24 optical neurons based on EOM | The network had a 294-fold acceleration against a conventional benchmark in performing a differential system emulation task |
| 2018 | LIN Xing，Science［82］ | 3D-printed diffractive deep neural network | Classification（Imaging）network：5-layers，200×200（300×300）neurons of a layer | In the task of handwritten digit classification，the accuracy was 91.75% for a five-layer design，and 93.39% for a seven-layer design |
| 2019 | FELDMANN J，Nature［80］ | Optical spiking neural network based on PCMs and MRRs | 4 spiking neurons based on MRRs with PCMs60 optical synapses based on PCMs and integrate waveguide | The network can implement supervised and unsupervised learning and was able to successfully classify the four 15-pixel images |
| 2019 | ZUO Ying，Optica［83］ | All-optical neural network（AONN）with linear operations and nonlinear activationfunctions | Two-layer AONN：16×4×2 | A two-layer AONN can classify the phases of a prototypical Ising model，and successfully capture the essential features that distinguish the order and disorder phases |
| 2019 | BERNSTEIN L，Physical Review X［84］ | A new type of photonic accelerator based on coherent detection | The number of neurons can be extended to ≥106 by the massive spatial multiplexing enabled by standard free-space optical components | The standard quantum limit can be as low as 50 zJ/MAC when neural networks are trained on the MNIST dataset |
| 2020 | RAFAYALYAN M，Physical Review X［85］ | Reservoir network based on the spatial light modulator and scattering medium | Up to 50000 optical nodes | The network successfully predicted on large spatiotemporal chaotic datasets |
| 2020 | SHI Bin，IEEE JSTQE［86］ | Feedforward neural network based on SOAs | 8×8 InP on-chip weighted circuitsThe non-linear function of neuron was implemented via software | The prediction accuracy of the Iris flower classification problem by the 3-layer photonic deep neural network was 85.8% |
| 2020 | BANGARI V，IEEE JSTQE［62］ | Digital electronics and analog photonics for convolutional neural networks（DEAP-CNNs） | There are up to 1200 MRRs in the weight bank array theoretically | DEAP-CNN was 2.8 to 14 times faster while using almost 25% less energy than current state-of-the-art graphical processing units. Overall accuracy was 97.6% for a test set of 500 images in the MNIST task |
| 2021 | FELDMANN J，Nature［87］ | A specific integrated photonic hardware accelerator based on photonic tensor core | 16×16 PCM integrated array to realize the matrix vector multiplication | The accelerator operated at the speed of 1012 MAC operations per second |
| 2021 | XU Xingyuan，Nature［88］ | Optical vector convolutional accelerator | Ten 3×3 convolutional kernels.Optical frequency combs provide 90 optical signals with different wavelengthsUsing wavelength division multiplexing，time division multiplexing and space division multiplexing | The computing speed of a single processor exceeded 10 TOPS，and the accuracy of handwritten digit images recognition was 88% |
