mlsanityData to 14 Oct 2026

Toward Lifelong-Sustainable Electronic-Photonic AI Systems via Extreme Efficiency, Reconfigurability, and Robustness

arXiv 2509.07396 ↗First posted 9 Sep 2025physics.optics cs.AI cs.ETcited by 1

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Abstract (arXiv)

The relentless growth of large-scale artificial intelligence (AI) has created unprecedented demand for computational power, straining the energy, bandwidth, and scaling limits of conventional electronic platforms. Electronic-photonic integrated circuits (EPICs) have emerged as a compelling platform for next-generation AI systems, offering inherent advantages in ultra-high bandwidth, low latency, and energy efficiency for computing and interconnection. Beyond performance, EPICs also hold unique promises for sustainability. Fabricated in relaxed process nodes with fewer metal layers and lower defect densities, photonic devices naturally reduce embodied carbon footprint (CFP) compared to advanced digital electronic integrated circuits, while delivering orders-of-magnitude higher computing performance and interconnect bandwidth. To further advance the sustainability of photonic AI systems, we explore how electronic-photonic design automation (EPDA) and cross-layer co-design methodologies can amplify these inherent benefits. We present how advanced EPDA tools enable more compact layout generation, reducing both chip area and metal layer usage. We will also demonstrate how cross-layer device-circuit-architecture co-design unlocks new sustainability gains for photonic hardware: ultra-compact photonic circuit designs that minimize chip area cost, reconfigurable hardware topology that adapts to evolving AI workloads, and intelligent resilience mechanisms that prolong lifetime by tolerating variations and faults. By uniting intrinsic photonic efficiency with EPDA- and co-design-driven gains in area efficiency, reconfigurability, and robustness, we outline a vision for lifelong-sustainable electronic-photonic AI systems. This perspective highlights how EPIC AI systems can simultaneously meet the performance demands of modern AI and the urgent imperative for sustainable computing.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 43 references that are in the map. First posted on arXiv 9 Sep 2025.

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12 of 43 references.

References · 12

  1. 2023TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddingscited by 202
  2. 2018All-Optical Machine Learning Using Diffractive Deep Neural Networkscited by 177
  3. 2020Photonics for artificial intelligence and neuromorphic computingcited by 112
  4. 2021L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimizationcited by 21
  5. 2023Spatially Varying Nanophotonic Neural Networkscited by 12
  6. 2023Optical Transformerscited by 10
  7. 2024TeMPO: Efficient Time-Multiplexed Dynamic Photonic Tensor Core for Edge AI with Compact Slow-Light Electro-Optic Modulatorcited by 6
  8. 2024SCATTER: Algorithm-Circuit Co-Sparse Photonic Accelerator with Thermal-Tolerant, Power-Efficient In-situ Light Redistributioncited by 6
  9. 2023ECO-CHIP: Estimation of Carbon Footprint of Chiplet-based Architectures for Sustainable VLSIcited by 2
  10. 2025Hardware-Efficient Photonic Tensor Core: Accelerating Deep Neural Networks with Structured Compressioncited by 2
  11. 2024Photonics for Sustainable Computingcited by 1
  12. 2024DOCTOR: Dynamic On-Chip Temporal Variation Remediation Toward Self-Corrected Photonic Tensor Acceleratorscited by 1

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Citing papers per year: 2026: 1.

1 paper.