QAMNet: Fast and efficient optical QAM neural networks

The energy consumption of neural network inference has become a topic of paramount importance with the growing success and adoption of deep neural networks. Analog optical neural networks (ONNs) can reduce the energy of matrix-vector multiplication in neural network inference below that of digital electronics. However, realizing this promise remains challenging due to digital-to-analog conversion: even at low bit precisions, encoding the levels of digital weights and inputs into the analog domain requires specialized and power-hungry electronics. Faced with similar challenges, the field of telecommunications has developed the complex-valued Quadrature-Amplitude Modulation (QAM), the workhorse modulation format for decades. QAM maximally exploits the complex amplitude to provide a quadratic energy saving over intensity-only modulation. Inspired by this advantage, this work introduces QAMNet, an optical neural network hardware and architecture with superior energy consumption to existing ONNs, that fully utilizes the complex nature of the amplitude of light with QAM.