# ReSiPI: A Reconfigurable Silicon-Photonic 2.5D Chiplet Network with PCMs for Energy-Efficient Interposer Communication

arXiv: https://arxiv.org/abs/2208.04231 · HTML: https://mlsanity.com/paper/2208.04231/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2022.

## Built on by (most cited first, 3 of 3)

- [Machine Learning Accelerators in 2.5D Chiplet Platforms with Silicon Photonics](https://mlsanity.com/paper/2301.12252/) (2023)
- [Silicon Photonic 2.5D Interposer Networks for Overcoming Communication Bottlenecks in Scale-out Machine Learning Hardware Accelerators](https://mlsanity.com/paper/2403.04189/) (2024)
- [PhotoGAN: Generative Adversarial Neural Network Acceleration with Silicon Photonics](https://mlsanity.com/paper/2501.13828/) (2025)

## Abstract (arXiv)

2.5D chiplet systems have been proposed to improve the low manufacturing yield of large-scale chips. However, connecting the chiplets through an electronic interposer imposes a high traffic load on the interposer network. Silicon photonics technology has shown great promise towards handling a high volume of traffic with low latency in intra-chip network-on-chip (NoC) fabrics. Although recent advances in silicon photonic devices have extended photonic NoCs to enable high bandwidth communication in 2.5D chiplet systems, such interposer-based photonic networks still suffer from high power consumption. In this work, we design and analyze a novel Reconfigurable power-efficient and congestion-aware Silicon Photonic 2.5D Interposer network, called ReSiPI. Considering run-time traffic, ReSiPI is able to dynamically deploy inter-chiplet photonic gateways to improve the overall network congestion. ReSiPI also employs switching elements based on phase change materials (PCMs) to dynamically reconfigure and power-gate the photonic interposer network, thereby improving the network power efficiency. Compared to the best prior state-of-the-art 2.5D photonic network, ReSiPI demonstrates, on average, 37% lower latency, 25% power reduction, and 53% energy minimization in the network.