# Harnessing Photonics for Machine Intelligence

arXiv: https://arxiv.org/abs/2604.10841 · HTML: https://mlsanity.com/paper/2604.10841/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 42 of its 197 references that are in the map.
- First posted on arXiv 12 Apr 2026.

## Builds on (most cited first, 30 of 42)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale](https://mlsanity.com/paper/2208.07339/) (2022)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models](https://mlsanity.com/paper/2408.00724/) (2024)
- [Neural Operators for Accelerating Scientific Simulations and Design](https://mlsanity.com/paper/2309.15325/) (2023)
- [Photonics for artificial intelligence and neuromorphic computing](https://mlsanity.com/paper/2011.00111/) (2020)
- [Machine Learning for Electronic Design Automation: A Survey](https://mlsanity.com/paper/2102.03357/) (2021)
- [Neuromorphic Silicon Photonic Networks](https://mlsanity.com/paper/1611.02272/) (2016)
- [CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices](https://mlsanity.com/paper/1708.08917/) (2017)
- [The physics of optical computing](https://mlsanity.com/paper/2308.00088/) (2023)
- [CrossLight: A Cross-Layer Optimized Silicon Photonic Neural Network Accelerator](https://mlsanity.com/paper/2102.06960/) (2021)
- [Image sensing with multilayer, nonlinear optical neural networks](https://mlsanity.com/paper/2207.14293/) (2022)
- [NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation](https://mlsanity.com/paper/2209.10098/) (2022)
- [Photonic tensor cores for machine learning](https://mlsanity.com/paper/2002.03780/) (2020)
- [An Electro-Photonic System for Accelerating Deep Neural Networks](https://mlsanity.com/paper/2109.01126/) (2021)
- [Lightening-Transformer: A Dynamically-operated Optically-interconnected Photonic Transformer Accelerator](https://mlsanity.com/paper/2305.19533/) (2023)
- [A compact butterfly-style silicon photonic-electronic neural chip for hardware-efficient deep learning](https://mlsanity.com/paper/2111.06705/) (2021)
- [Inverse design of photonic devices with strict foundry fabrication constraints](https://mlsanity.com/paper/2201.12965/) (2022)
- [WaveY-Net: Physics-augmented deep learning for high-speed electromagnetic simulation and optimization](https://mlsanity.com/paper/2203.01248/) (2022)
- [SimPhony: A Device-Circuit-Architecture Cross-Layer Modeling and Simulation Framework for Heterogeneous Electronic-Photonic AI System](https://mlsanity.com/paper/2411.13715/) (2024)
- [Can Test-Time Scaling Improve World Foundation Model?](https://mlsanity.com/paper/2503.24320/) (2025)
- [Inverse Design in Nanophotonics via Representation Learning](https://mlsanity.com/paper/2507.00546/) (2025)
- [AI Agents for Photonic Integrated Circuit Design Automation](https://mlsanity.com/paper/2508.14123/) (2025)
- [O-HAS: Optical Hardware Accelerator Search for Boosting Both Acceleration Performance and Development Speed](https://mlsanity.com/paper/2108.07538/) (2021)
- [In-memory photonic dot-product engine with electrically programmable weight banks](https://mlsanity.com/paper/2304.14302/) (2023)
- [TeMPO: Efficient Time-Multiplexed Dynamic Photonic Tensor Core for Edge AI with Compact Slow-Light Electro-Optic Modulator](https://mlsanity.com/paper/2402.07393/) (2024)
- [SCATTER: Algorithm-Circuit Co-Sparse Photonic Accelerator with Thermal-Tolerant, Power-Efficient In-situ Light Redistribution](https://mlsanity.com/paper/2407.05510/) (2024)

## Abstract (arXiv)

The exponential growth of machine-intelligence workloads is colliding with the power, memory, and interconnect limits of the post-Moore era, motivating compute substrates that scale beyond transistor density alone. Integrated photonics is emerging as a candidate for artificial intelligence (AI) acceleration by exploiting optical bandwidth and parallelism to reshape data movement and computation. This review reframes photonic computing from a circuits-and-systems perspective, moving beyond building-block progress toward cross-layer system analysis and full-stack design automation. We synthesize recent advances through a bottleneck-driven taxonomy that delineates the operating regimes and scaling trends where photonics can deliver end-to-end sustained benefits. A central theme is cross-layer co-design and workload-adaptive programmability to sustain high efficiency and versatility across evolving application domains at scale. We further argue that Electronic-Photonic Design Automation (EPDA) will be pivotal, enabling closed-loop co-optimization across simulation, inverse design, system modeling, and physical implementation. By charting a roadmap from laboratory prototypes to scalable, reproducible electronic-photonic ecosystems, this review aims to guide the CAS community toward an automated, system-centric era of photonic machine intelligence.