  Electronically reprogrammable photonic circuits based on phase-change
chalcogenides present an avenue to resolve the von-Neumann bottleneck; however,
implementation of such hybrid photonic-electronic processing has not achieved
computational success. Here, we achieve this milestone by demonstrating an
in-memory photonic-electronic dot-product engine, one that decouples electronic
programming of phase-change materials (PCMs) and photonic computation.
Specifically, we develop non-volatile electronically reprogrammable PCM memory
cells with a record-high 4-bit weight encoding, the lowest energy consumption
per unit modulation depth (1.7 nJ per dB) for Erase operation
(crystallization), and a high switching contrast (158.5%) using non-resonant
silicon-on-insulator waveguide microheater devices. This enables us to perform
parallel multiplications for image processing with a superior contrast-to-noise
ratio (greater than 87.36) that leads to an enhanced computing accuracy
(standard deviation less than 0.007). An in-memory hybrid computing system is
developed in hardware for convolutional processing for recognizing images from
the MNIST database with inferencing accuracies of 86% and 87%.
