20 GHz In-situ Optical Backpropagation for Training AWGR-based Hyperdimensional Photonic Tensor Cores

We demonstrate in-situ optical backpropagation and train a hyperdimensional AWGR-based photonic tensor core, performing both forward and backward MVM operations at 20 GHz and presenting inference accuracies of 93.5% and 81.6% for the Wine and Moons datasets, respectively.

Authors

Odysseas Asimopoulos (ORCID: https://orcid.org/0009-0005-2615-0036)
L. Avramelou
N. Passalis
A. Tefas
K. Vyrskokinos
S. Kovaios
M. Moralis - Pegios
N. Pleros

Institutions

Aristotle University of Thessaloniki (GR)

Publication Details

Journal: Zenodo (CERN European Organization for Nuclear Research)
Published: 2026-11-10
DOI: https://doi.org/10.5281/zenodo.24103607
Primary Topic: Neural Networks and Reservoir Computing
Type: article
Field-Weighted Citation Impact: 0.00

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article

20 GHz In-situ Optical Backpropagation for Training AWGR-based Hyperdimensional Photonic Tensor Cores

Odysseas Asimopoulos, L. Avramelou, N. Passalis, A. Tefas et al.

Nov 10, 2026

Zenodo (CERN European Organization for Nuclear Research)

Neural Networks and Reservoir Computing

article

20 GHz In-situ Optical Backpropagation for Training AWGR-based Hyperdimensional Photonic Tensor Cores

Odysseas Asimopoulos, L. Avramelou, N. Passalis, A. Tefas, K. Vyrskokinos, S. Kovaios, M. Moralis - Pegios, N. Pleros

November 10, 2026article en

Abstract

We demonstrate in-situ optical backpropagation and train a hyperdimensional AWGR-based photonic tensor core, performing both forward and backward MVM operations at 20 GHz and presenting inference accuracies of 93.5% and 81.6% for the Wine and Moons datasets, respectively.

Zenodo (CERN European Organization for Nuclear Research)

Aristotle University of Thessaloniki (GR)

Neural Networks and Reservoir Computing

DOI Open Access PDF OpenAlex

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