Hyperdimensional Photonic AI Computing
We discuss our work on hyperdimensional photonic AI accelerators, that leverage time-space- and wavelength-division multiplexing towards enhancing compute power and energy efficiency. We benchmark their performance against state-of-the-art GPUs, demonstrating superior throughput and low-energy operation
Authors
- Miltiadis Moralis‐Pegios (ORCID: https://orcid.org/0000-0002-9401-730X)
- Apostolos Tsakyridis (ORCID: https://orcid.org/0000-0003-1498-4904)
- Stefanos Kovaios (ORCID: https://orcid.org/0000-0002-3366-3244)
- Nikos Pleros (ORCID: https://orcid.org/0000-0003-2931-4540)
- Christos Pappas (ORCID: https://orcid.org/0000-0002-6319-0708)
- Thodoris Moschos
- Antonis Prapas
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-11-10
- DOI
- https://doi.org/10.5281/zenodo.24102407
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00