Abstract
We propose a novel photonic accelerator architecture based on a broadcast-and-weight approach for a deep neural network through a photonic integrated cross-connect. The single neuron and the complete neural network operation are numerically simulated. The weight calibration and weighted addition are reproduced and demonstrated to behave as in the experimental measurements. A dynamic range higher than 25 dB is predicted, in line with the measurements. The weighted addition operation is also simulated and analyzed as a function of the optical crosstalk and the number of input colors involved. In particular, while an increase in optical crosstalk negatively influences the simulated error, a greater number of channels results in better performance. The iris flower classification problem is solved by implementing the weight matrix of a trained three-layer deep neural network. The performance of the corresponding photonic implementation is numerically investigated by tuning the optical crosstalk and waveguide loss, in order to anticipate energy consumption per operation. The analysis of the prediction error as a function of the optical crosstalk per layer suggests that the first layer is essential to the final accuracy. The ultimate accuracy shows a quasi-linear dependence between the prediction accuracy and the errors per layer for a normalized root mean square error lower than 0.09, suggesting that there is a maximum level of error permitted at the first layer for guaranteeing a final accuracy higher than 89%. However, it is still possible to find good local minima even for an error higher than 0.09, due to the stochastic nature of the network we are analyzing. Lower levels of path losses allow for half the power consumption at the matrix multiplication unit, for the same error level, offering opportunities for further improved performance. The good agreement between the simulations and the experiments offers a solid base for studying the scalability of this kind of network.
Highlights
The boost in data volume of the information transient and data storage continuously stimulates the demand for high-speed information processing [1,2]
We have demonstrated the implementation of a photonic deep neural network (PDNN) via cross-connect circuits based on a broadcast-and-weight architecture, using semiconductor optical amplifiers (SOAs) and array waveguide gratings (AWGs) [27]
We propose a photonic deep neural network based on the use of wavelength division multiplexing (WDM) input signals, and an SOA-based matrix multiplication unit
Summary
The boost in data volume of the information transient and data storage continuously stimulates the demand for high-speed information processing [1,2]. Photonics technology provides a promising approach for neural network implementation as it offers parallel information processing when exploiting different domains (wavelength, polarization, phase, space), resulting in ultrabroad bandwidth that outperforms the electronics, while it decouples power consumption from computational speed. Micro-ring resonator-based optical neural networks with wavelength division multiplexing (WDM) operation have promised to increase interconnection bandwidth [26], thermal crosstalk and low dynamic range complicate the weight calibration. We have demonstrated the implementation of a photonic deep neural network (PDNN) via cross-connect circuits based on a broadcast-and-weight architecture, using SOAs and array waveguide gratings (AWGs) [27]. The single neuron and complete neural network operation are numerically simulated to provide guidelines on how to design future cross-connect photonic integrated chips for accelerating computation on-chip.
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