Silicon integrated photonic-electronic neuron for noise-resilient deep learning

Abstract

This paper presents an experimental demonstration of the photonic segment of a photonic-electronic multiply accumulate neuron (PEMAN) architecture, employing a silicon photonic chip with high-speed electro-absorption modulators for matrix-vector multiplications. The photonic integrated circuit has been evaluated through a noise-sensitive three-layer neural network (NN) with 1350 trainable parameters targeting heartbeat sound classification for health monitoring purposes. Its experimental validation revealed F1-scores of 85.9% and 81% at compute rates of 10 and 20 Gbaud, respectively, exploiting quantization- and noise-aware deep learning techniques and introducing a novel activation function slope stretching strategy for mitigating noise impairments. The enhanced noise-resilient properties of this novel training model are confirmed via simulations for varying noise levels, being in excellent agreement with the respective experimental data obtained at 10, 20, and 30 Gbaud symbol rates.