Exploring subthreshold processing for next-generation TinyAI
The energy demands of modern AI systems have reached unprecedented levels, driven by the rapid scaling of deep learning models, including large language models, and the inefficiencies of current computational architectures. In contrast, biological neural systems operate with remarkable energy effici...
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| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2025.1638782/full |
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| author | Farid Nakhle Antoine H. Harfouche Antoine H. Harfouche Hani Karam Vasileios Tserolas |
| author_facet | Farid Nakhle Antoine H. Harfouche Antoine H. Harfouche Hani Karam Vasileios Tserolas |
| author_sort | Farid Nakhle |
| collection | DOAJ |
| description | The energy demands of modern AI systems have reached unprecedented levels, driven by the rapid scaling of deep learning models, including large language models, and the inefficiencies of current computational architectures. In contrast, biological neural systems operate with remarkable energy efficiency, achieving complex computations while consuming orders of magnitude less power. A key mechanism enabling this efficiency is subthreshold processing, where neurons perform computations through graded, continuous signals below the spiking threshold, reducing energy costs. Despite its significance in biological systems, subthreshold processing remains largely overlooked in AI design. This perspective explores how principles of subthreshold dynamics can inspire the design of novel AI architectures and computational methods as a step toward advancing TinyAI. We propose pathways such as algorithmic analogs of subthreshold integration, including graded activation functions, dendritic-inspired hierarchical processing, and hybrid analog-digital systems to emulate the energy-efficient operations of biological neurons. We further explore neuromorphic and compute-in-memory hardware platforms that could support these operations, and propose a design stack aligned with the efficiency and adaptability of the brain. By integrating subthreshold dynamics into AI architecture, this work provides a roadmap toward sustainable, responsive, and accessible intelligence for resource-constrained environments. |
| format | Article |
| id | doaj-art-3a0b4a950d3046049e9afaba2b2e227f |
| institution | Kabale University |
| issn | 1662-5188 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj-art-3a0b4a950d3046049e9afaba2b2e227f2025-08-20T03:34:49ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882025-07-011910.3389/fncom.2025.16387821638782Exploring subthreshold processing for next-generation TinyAIFarid Nakhle0Antoine H. Harfouche1Antoine H. Harfouche2Hani Karam3Vasileios Tserolas4Department of Computer Science, Temple University, Japan Campus, Tokyo, JapanUnité de Formation et de Recherche en Sciences Économiques, Gestion, Mathématiques, et Informatique, Université Paris Nanterre, Nanterre, FranceFaculty of Business and Economics, American University of Science and Technology, Beirut, LebanonDepartment of Computer Science, Temple University, Japan Campus, Tokyo, JapanDepartment of Computer Science, Temple University, Japan Campus, Tokyo, JapanThe energy demands of modern AI systems have reached unprecedented levels, driven by the rapid scaling of deep learning models, including large language models, and the inefficiencies of current computational architectures. In contrast, biological neural systems operate with remarkable energy efficiency, achieving complex computations while consuming orders of magnitude less power. A key mechanism enabling this efficiency is subthreshold processing, where neurons perform computations through graded, continuous signals below the spiking threshold, reducing energy costs. Despite its significance in biological systems, subthreshold processing remains largely overlooked in AI design. This perspective explores how principles of subthreshold dynamics can inspire the design of novel AI architectures and computational methods as a step toward advancing TinyAI. We propose pathways such as algorithmic analogs of subthreshold integration, including graded activation functions, dendritic-inspired hierarchical processing, and hybrid analog-digital systems to emulate the energy-efficient operations of biological neurons. We further explore neuromorphic and compute-in-memory hardware platforms that could support these operations, and propose a design stack aligned with the efficiency and adaptability of the brain. By integrating subthreshold dynamics into AI architecture, this work provides a roadmap toward sustainable, responsive, and accessible intelligence for resource-constrained environments.https://www.frontiersin.org/articles/10.3389/fncom.2025.1638782/fulldendritic processingenergy efficiencygraded activationshybrid analog-digital systemsneuromorphic computingsubthreshold processing |
| spellingShingle | Farid Nakhle Antoine H. Harfouche Antoine H. Harfouche Hani Karam Vasileios Tserolas Exploring subthreshold processing for next-generation TinyAI Frontiers in Computational Neuroscience dendritic processing energy efficiency graded activations hybrid analog-digital systems neuromorphic computing subthreshold processing |
| title | Exploring subthreshold processing for next-generation TinyAI |
| title_full | Exploring subthreshold processing for next-generation TinyAI |
| title_fullStr | Exploring subthreshold processing for next-generation TinyAI |
| title_full_unstemmed | Exploring subthreshold processing for next-generation TinyAI |
| title_short | Exploring subthreshold processing for next-generation TinyAI |
| title_sort | exploring subthreshold processing for next generation tinyai |
| topic | dendritic processing energy efficiency graded activations hybrid analog-digital systems neuromorphic computing subthreshold processing |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2025.1638782/full |
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