# Photonic tensor cores for machine learning

arXiv: https://arxiv.org/abs/2002.03780 · HTML: https://mlsanity.com/paper/2002.03780/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2020.

## Built on by (most cited first, 16 of 16)

- [A compact butterfly-style silicon photonic-electronic neural chip for hardware-efficient deep learning](https://mlsanity.com/paper/2111.06705/) (2021)
- [Dynamic Precision Analog Computing for Neural Networks](https://mlsanity.com/paper/2102.06365/) (2021)
- [Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Network](https://mlsanity.com/paper/2203.13482/) (2022)
- [Machine learning in solar physics](https://mlsanity.com/paper/2306.15308/) (2023)
- [SimPhony: A Device-Circuit-Architecture Cross-Layer Modeling and Simulation Framework for Heterogeneous Electronic-Photonic AI System](https://mlsanity.com/paper/2411.13715/) (2024)
- [Photonic Reconfigurable Accelerators for Efficient Inference of CNNs with Mixed-Sized Tensors](https://mlsanity.com/paper/2207.05278/) (2022)
- [M3ICRO: Machine Learning-Enabled Compact Photonic Tensor Core based on PRogrammable Multi-Operand Multimode Interference](https://mlsanity.com/paper/2305.19505/) (2023)
- [Integrated multi-operand optical neurons for scalable and hardware-efficient deep learning](https://mlsanity.com/paper/2305.19592/) (2023)
- [Towards Memory-Efficient Neural Networks via Multi-Level in situ Generation](https://mlsanity.com/paper/2108.11430/) (2021)
- [Small-scale photonic Kolmogorov-Arnold networks using standard telecom nonlinear modules](https://mlsanity.com/paper/2604.08432/) (2026)
- [ELight: Enabling Efficient Photonic In-Memory Neurocomputing with Life Enhancement](https://mlsanity.com/paper/2112.08512/) (2021)
- [All-optical temporal integration mediated by subwavelength heat antennas](https://mlsanity.com/paper/2505.04405/) (2025)
- [SKYLIGHT: A Scalable Hundred-Channel 3D Photonic In-Memory Tensor Core Architecture for Real-time AI Inference](https://mlsanity.com/paper/2602.19031/) (2026)
- [Photonic convolutional neural network with pre-trained in situ training](https://mlsanity.com/paper/2604.02429/) (2026)
- [Harnessing Photonics for Machine Intelligence](https://mlsanity.com/paper/2604.10841/) (2026)
- [Photonic AI: A Hybrid Diffractive Holographic Neural System for Passive Optical Real-Time Image Classification](https://mlsanity.com/paper/2604.15364/) (2026)

## Abstract (arXiv)

With an ongoing trend in computing hardware towards increased heterogeneity, domain-specific co-processors are emerging as alternatives to centralized paradigms. The tensor core unit (TPU) has shown to outperform graphic process units by almost 3-orders of magnitude enabled by higher signal throughout and energy efficiency. In this context, photons bear a number of synergistic physical properties while phase-change materials allow for local nonvolatile mnemonic functionality in these emerging distributed non van-Neumann architectures. While several photonic neural network designs have been explored, a photonic TPU to perform matrix vector multiplication and summation is yet outstanding. Here we introduced an integrated photonics-based TPU by strategically utilizing a) photonic parallelism via wavelength division multiplexing, b) high 2 Peta-operations-per second throughputs enabled by 10s of picosecond-short delays from optoelectronics and compact photonic integrated circuitry, and c) zero power-consuming novel photonic multi-state memories based on phase-change materials featuring vanishing losses in the amorphous state. Combining these physical synergies of material, function, and system, we show that the performance of this 8-bit photonic TPU can be 2-3 orders higher compared to an electrical TPU whilst featuring similar chip areas. This work shows that photonic specialized processors have the potential to augment electronic systems and may perform exceptionally well in network-edge devices in the looming 5G networks and beyond.