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Challenges and opportunities for memristors in modern AI computing paradigms

Year: 2025

Type: article

Abstract: The emergence of transformer-based artificial intelligence (AI) models has made a great impact on modern AI computing paradigms, where the attention mechanisms in transformer models dynamically generate weights and require computations involving global parameters. These requirements pose unprecedented challenges for memristor performance in in-memory computing, which demands memristor arrays with exceptional endurance, latency, energy consumption, and device uniformity. This perspective focuses on the alignment between AI computational requirements and memristor specifications, highlighting recent breakthroughs in materials, mechanisms, and applications for next-generation in-memory computing. Through this comprehensive analysis, our perspective provides critical insights into advancing memristor-based computing research toward practical AI applications. It also underscores key research priorities and the necessity for interdisciplinary collaboration to propel the future of AI innovation.

Source: Frontiers of Physics

DOI: 10.15302/frontphys.2026.035401

Authors: Shu Wang, Jia Wei, Lena Du, Cong Wang, Guozhong Zhao

Institutions: Chinese Academy of Sciences, National Center for Nanoscience and Technology, Capital Normal University, Xi'an University of Technology, Beijing University of Chemical Technology

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities

Awards: G2248328663, G6601859132