| The computationally demanding nature of Deep Learning (DL) has fueled the research on neuromorphics due to their potential to provide high-speed and low energy hardware accelerators. To this end, neuromorphic photonics are increasingly gain attention since they can operate in very high frequencies with very low energy consumption. However, they also introduce new challenges in DL training and deployment. In this paper, we propose a novel training method that is able to compensate for quantization noise, which profoundly exists in photonic hardware due to analog-to-digital (ADC) and digital-to-analog (DAC) conversions, targeting photonic neural networks (PNNs) which employ easily saturated activation functions. The proposed method takes into account quantization during training, leading to significant performance improvements during the inference phase. We conduct evaluation experiments on both image classification and time-series analysis tasks, employing a wide range of existing photonic neuromorphic architectures. The evaluation experiments demonstrate the effectiveness of the proposed method when low-bit resolution photonic architectures are used, as well as its generalization ability. |
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