# Machine Learning Accelerators in 2.5D Chiplet Platforms with Silicon Photonics

arXiv: https://arxiv.org/abs/2301.12252 · HTML: https://mlsanity.com/paper/2301.12252/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 47 references that are in the map.
- First posted on arXiv 28 Jan 2023.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [CrossLight: A Cross-Layer Optimized Silicon Photonic Neural Network Accelerator](https://mlsanity.com/paper/2102.06960/) (2021)
- [A Survey on Silicon Photonics for Deep Learning](https://mlsanity.com/paper/2101.01751/) (2021)
- [Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)](https://mlsanity.com/paper/1907.01525/) (2019)
- [ROBIN: A Robust Optical Binary Neural Network Accelerator](https://mlsanity.com/paper/2107.05530/) (2021)
- [An Electro-Photonic System for Accelerating Deep Neural Networks](https://mlsanity.com/paper/2109.01126/) (2021)
- [INDRA: Intrusion Detection using Recurrent Autoencoders in Automotive Embedded Systems](https://mlsanity.com/paper/2007.08795/) (2020)
- [RecLight: A Recurrent Neural Network Accelerator with Integrated Silicon Photonics](https://mlsanity.com/paper/2209.00084/) (2022)
- [A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantization](https://mlsanity.com/paper/2205.11244/) (2022)
- [ReSiPI: A Reconfigurable Silicon-Photonic 2.5D Chiplet Network with PCMs for Energy-Efficient Interposer Communication](https://mlsanity.com/paper/2208.04231/) (2022)
- [DeFT: A Deadlock-Free and Fault-Tolerant Routing Algorithm for 2.5D Chiplet Networks](https://mlsanity.com/paper/2112.09234/) (2021)

## Built on by (most cited first, 5 of 5)

- [Accelerating Neural Networks for Large Language Models and Graph Processing with Silicon Photonics](https://mlsanity.com/paper/2401.06885/) (2024)
- [Silicon Photonic 2.5D Interposer Networks for Overcoming Communication Bottlenecks in Scale-out Machine Learning Hardware Accelerators](https://mlsanity.com/paper/2403.04189/) (2024)
- [Towards Efficient Hyperdimensional Computing Using Photonics](https://mlsanity.com/paper/2311.17801/) (2023)
- [Optical Computing for Deep Neural Network Acceleration: Foundations, Recent Developments, and Emerging Directions](https://mlsanity.com/paper/2407.21184/) (2024)
- [Hardware Trojan Threats to Multi-Chiplet Photonic Neural Network Accelerators](https://mlsanity.com/paper/2609.06796/) (2026)

## Abstract (arXiv)

Domain-specific machine learning (ML) accelerators such as Google's TPU and Apple's Neural Engine now dominate CPUs and GPUs for energy-efficient ML processing. However, the evolution of electronic accelerators is facing fundamental limits due to the limited computation density of monolithic processing chips and the reliance on slow metallic interconnects. In this paper, we present a vision of how optical computation and communication can be integrated into 2.5D chiplet platforms to drive an entirely new class of sustainable and scalable ML hardware accelerators. We describe how cross-layer design and fabrication of optical devices, circuits, and architectures, and hardware/software codesign can help design efficient photonics-based 2.5D chiplet platforms to accelerate emerging ML workloads.