In-memory photonic dot-product engine with electrically programmable weight banks

Abstract

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%.

References in corpus (14)

• Deep Learning with Coherent Nanophotonic Circuits
• All-Optical Machine Learning Using Diffractive Deep Neural Networks
• Parallel convolution processing using an integrated photonic tensor core
• Photonics for artificial intelligence and neuromorphic computing
• 11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks
• Single-chip photonic deep neural network for instantaneous image classification
• Training of photonic neural networks through in situ backpropagation
• Programmable Phase-change Metasurfaces on Waveguides for Multimode Photonic Convolutional Neural Network
• A silicon photonic modulator neuron
• Nonvolatile electrically reconfigurable integrated photonic switch
• Non-volatile reconfigurable integrated photonics enabled by broadband low-loss phase change material
• Silicon microring synapses enable photonic deep learning beyond 9-bit precision
• Broadband nonvolatile electrically programmable silicon photonic switches
• Scalable massively parallel computing using continuous-time data representation in nanoscale crossbar array

Cited by in corpus (4)

• Nonlocal phase-change metaoptics for reconfigurable nonvolatile image processing
• Multiscale simulations of growth-dominated Sb${}_2$Te phase-change material for non-volatile photonic applications
• Ultra-high endurance silicon photonic memory using vanadium dioxide
• Waveguide-multiplexed photonic matrix-vector multiplication processor using multiport photodetectors