3 papers
physics.optics2025
Fully analog end-to-end online training with real-time adaptibility on integrated photonic platform
Zhimu Guo, A. Aadhi, Adam N. McCaughan +4
Analog neuromorphic photonic processors are uniquely positioned to harness the ultrafast bandwidth and inherent parallelism of light, enabling scalability, on-chip integration and…
cs.LG2025
Scaling of hardware-compatible perturbative training algorithms
Bakhrom G. Oripov, Andrew Dienstfrey, Adam N. McCaughan +1
In this work, we explore the capabilities of multiplexed gradient descent (MGD), a scalable and efficient perturbative zeroth-order training method for estimating the gradient of a…
cs.ET2025
Roadmap on Neuromorphic Photonics
Daniel Brunner, Bhavin J. Shastri, Mohammed A. Al Qadasi +147
This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementatio…