Computing at the Edge Enabled by Indoor Photovoltaics and Ferroelectrics
Abstract
The exponential, pervasive rise of artificial intelligence (AI), hosted in large data centres, will test the limits of the underpinning infrastructure, including electricity and water supplies, which are becoming increasingly precious resources. Rather than performing all AI computations in centralized servers, for many functionalities, these can be performed locally across a network of billions of small, autonomous nodes, saving substantial energy costs associated with communication. This alternative paradigm is known as edge computing, or distributed intelligence (DI), but has been held back by the lack of availability of reliable local energy supplies matching the energy requirements of the computational infrastructure. Opportunities to address this challenge are emerging with rapid advances in high-performance indoor photovoltaics (IPVs) for local energy harvesting, as well as reductions in computational cost through neuromorphic or in-memory computing devices. In this perspective, we discuss the requirements of IPVs for DI, the extent to which emerging materials fulfil these requirements, and how current gaps could be addressed. We make the case that (anti)ferroelectric materials can surpass bottlenecks for both electrostatic energy storage and low-power in-memory neuromorphic computing. The core theme of this perspective is that co-creation between energy harvesting, storage and computing is essential for making DI a reliable and more sustainable alternative to centralized AI.
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BibTeX
@misc{hoye2026computing,
title = {{Computing at the Edge Enabled by Indoor Photovoltaics and Ferroelectrics}},
author = {Robert L. Z. Hoye and Markus Hellenbrand and Nuno Estrócio and Austin M. Kay and Alice Scardina and Alessandro Mezzetti and Ignasi Fina and Jorge Íñiguez-González and Luís S. Marques and Francesco Matteucci and Quanxi Jia and Judith L. MacManus-Driscoll and Bert Offrein and Florencio Sánchez and Beatriz Noheda and George Koutsourakis and Marina Freitag and Gregory Burwell and Giulia Grancini and Jose P. B. Silva},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.05133},
eprint = {2610.05133},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.05133},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Computing at the Edge Enabled by Indoor Photovoltaics and Ferroelectrics
AU - Robert L. Z. Hoye
AU - Markus Hellenbrand
AU - Nuno Estrócio
AU - Austin M. Kay
AU - Alice Scardina
AU - Alessandro Mezzetti
AU - Ignasi Fina
AU - Jorge Íñiguez-González
AU - Luís S. Marques
AU - Francesco Matteucci
AU - Quanxi Jia
AU - Judith L. MacManus-Driscoll
AU - Bert Offrein
AU - Florencio Sánchez
AU - Beatriz Noheda
AU - George Koutsourakis
AU - Marina Freitag
AU - Gregory Burwell
AU - Giulia Grancini
AU - Jose P. B. Silva
PY - 2026
DA - 2026/10/04
PB - arXiv
DO - 10.48550/arxiv.2610.05133
UR - https://arxiv.org/abs/2610.05133
AB - The exponential, pervasive rise of artificial intelligence (AI), hosted in large data centres, will test the limits of the underpinning infrastructure, including electricity and water supplies, which are becoming increasingly precious resources. Rather than performing all AI computations in centralized servers, for many functionalities, these can be performed locally across a network of billions of small, autonomous nodes, saving substantial energy costs associated with communication. This alternative paradigm is known as edge computing, or distributed intelligence (DI), but has been held back by the lack of availability of reliable local energy supplies matching the energy requirements of the computational infrastructure. Opportunities to address this challenge are emerging with rapid advances in high-performance indoor photovoltaics (IPVs) for local energy harvesting, as well as reductions in computational cost through neuromorphic or in-memory computing devices. In this perspective, we discuss the requirements of IPVs for DI, the extent to which emerging materials fulfil these requirements, and how current gaps could be addressed. We make the case that (anti)ferroelectric materials can surpass bottlenecks for both electrostatic energy storage and low-power in-memory neuromorphic computing. The core theme of this perspective is that co-creation between energy harvesting, storage and computing is essential for making DI a reliable and more sustainable alternative to centralized AI.
ER -