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

arXiv: https://arxiv.org/abs/2304.14302 · HTML: https://mlsanity.com/paper/2304.14302/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2023.

## Built on by (most cited first, 6 of 6)

- [Hyperspectral In-Memory Computing with Optical Frequency Combs and Programmable Optical Memories](https://mlsanity.com/paper/2310.11014/) (2023)
- [On-Chip Learning with Memristor-Based Neural Networks: Assessing Accuracy and Efficiency Under Device Variations, Conductance Errors, and Input Noise](https://mlsanity.com/paper/2408.14680/) (2024)
- [Hardware-Software Collaborative Computing of Photonic Spiking Reinforcement Learning for Robotic Continuous Control](https://mlsanity.com/paper/2512.00427/) (2025)
- [Toward all-optical unsupervised Hebbian learning in deep photonic neuromorphic networks](https://mlsanity.com/paper/2601.22300/) (2026)
- [SKYLIGHT: A Scalable Hundred-Channel 3D Photonic In-Memory Tensor Core Architecture for Real-time AI Inference](https://mlsanity.com/paper/2602.19031/) (2026)
- [Harnessing Photonics for Machine Intelligence](https://mlsanity.com/paper/2604.10841/) (2026)

## Abstract (arXiv)

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%.