@inproceedings{saha-etal-2026-energy,
title = "The Energy-Accuracy Frontier: Benchmarking {SNN}s on Low-Resource {I}ndian Linguistic Tasks",
author = "Saha, Priyam and
Sahoo, Debiprasanna and
Jana, Abhik",
editor = "Zhang, Chen and
Allaway, Emily and
M{'}hamdi, Meryem and
Limkonchotiwat, Peerat and
Bai, Richard He and
T.Y.S.S., Santosh and
Thapa, Surendrabikram and
Li, Yinqiao",
booktitle = "Proceedings of the 10th Widening {NLP} Workshop",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.winlp-main.6/",
pages = "61--67",
ISBN = "979-8-89176-472-9",
abstract = "The increasing complexity of state-of-the-art neural networks creates significant computational energy overhead in Natural Language Processing (NLP) applications. A few studies have explored opportunities to reduce overhead by using a Spiking Neural Network (SNN) instead of a mainstream artificial neural network (ANN) in computer vision and sensory domains. A comprehensive analysis of SNN{'}s usability for NLP tasks in low-resource languages has yet to be conducted. Hence, in this paper, we present the first-ever comparative energy-overhead analysis of ANNs vs. SNNs for text classification tasks in low-resource Indian languages such as Hindi, Bengali, and Telugu. Our experiments show that although we lose classification accuracy by an acceptable margin when replacing an ANN with an SNN, we save significant energy, which highlights the potential for efficient utilization of edge computing devices to run NLP tasks for low-resource languages. This work shows a direction for advancing energy-efficient NLP applications, especially for low-resource languages. We make code and dataset publicly available [{\ensuremath{<}} https://github.com/PS1076/Energy-Accuracy-Frontier{\ensuremath{>}}]."
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<abstract>The increasing complexity of state-of-the-art neural networks creates significant computational energy overhead in Natural Language Processing (NLP) applications. A few studies have explored opportunities to reduce overhead by using a Spiking Neural Network (SNN) instead of a mainstream artificial neural network (ANN) in computer vision and sensory domains. A comprehensive analysis of SNN’s usability for NLP tasks in low-resource languages has yet to be conducted. Hence, in this paper, we present the first-ever comparative energy-overhead analysis of ANNs vs. SNNs for text classification tasks in low-resource Indian languages such as Hindi, Bengali, and Telugu. Our experiments show that although we lose classification accuracy by an acceptable margin when replacing an ANN with an SNN, we save significant energy, which highlights the potential for efficient utilization of edge computing devices to run NLP tasks for low-resource languages. This work shows a direction for advancing energy-efficient NLP applications, especially for low-resource languages. We make code and dataset publicly available [\ensuremath< https://github.com/PS1076/Energy-Accuracy-Frontier\ensuremath>].</abstract>
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%0 Conference Proceedings
%T The Energy-Accuracy Frontier: Benchmarking SNNs on Low-Resource Indian Linguistic Tasks
%A Saha, Priyam
%A Sahoo, Debiprasanna
%A Jana, Abhik
%Y Zhang, Chen
%Y Allaway, Emily
%Y M’hamdi, Meryem
%Y Limkonchotiwat, Peerat
%Y Bai, Richard He
%Y T.Y.S.S., Santosh
%Y Thapa, Surendrabikram
%Y Li, Yinqiao
%S Proceedings of the 10th Widening NLP Workshop
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-472-9
%F saha-etal-2026-energy
%X The increasing complexity of state-of-the-art neural networks creates significant computational energy overhead in Natural Language Processing (NLP) applications. A few studies have explored opportunities to reduce overhead by using a Spiking Neural Network (SNN) instead of a mainstream artificial neural network (ANN) in computer vision and sensory domains. A comprehensive analysis of SNN’s usability for NLP tasks in low-resource languages has yet to be conducted. Hence, in this paper, we present the first-ever comparative energy-overhead analysis of ANNs vs. SNNs for text classification tasks in low-resource Indian languages such as Hindi, Bengali, and Telugu. Our experiments show that although we lose classification accuracy by an acceptable margin when replacing an ANN with an SNN, we save significant energy, which highlights the potential for efficient utilization of edge computing devices to run NLP tasks for low-resource languages. This work shows a direction for advancing energy-efficient NLP applications, especially for low-resource languages. We make code and dataset publicly available [\ensuremath< https://github.com/PS1076/Energy-Accuracy-Frontier\ensuremath>].
%U https://aclanthology.org/2026.winlp-main.6/
%P 61-67
Markdown (Informal)
[The Energy-Accuracy Frontier: Benchmarking SNNs on Low-Resource Indian Linguistic Tasks](https://aclanthology.org/2026.winlp-main.6/) (Saha et al., WiNLP 2026)
ACL