Technology Roadmap of Bioinspired Computing Hardware

The rapid growth of artificial intelligence (AI) is increasingly constrained by fundamental hardware bottlenecks in computation throughput and energy efficiency. Bioinspired computing (BIC) offers a promising alternative by emulating the intrinsic advantages of biological systems, such as parallelism, adaptability, and robustness. Progress in BIC hardware demands interdisciplinary convergence that bridges materials science and device physics with neuroscience, computer science, mathematics, and information science. Therefore, the development of this cross-disciplinary field urgently requires a comprehensive roadmap that analyzes systematically and in-depth the frontier issues and the latest progress. In this roadmap, we categorize the critical challenges into three components: hardware foundations, architectures, and prototype realizations. We highlight how biological features inspire the design of BIC hardware through device physics and discuss their performance metrics and engineering challenges. We then describe how diverse signaling rules and structural organizations in BIC architectures support specific computational prototypes, including electronic and photonic BIC chips, and present a technological roadmap that outlines opportunities to expand the functional scope of BIC hardware through coordinated advances in devices, architectures, and system demonstrations. This ongoing convergence of interdisciplinary knowledge can help accelerate the shift toward high-efficiency AI hardware.

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Publication Details

Journal
ACS Nano
Published
2026-03-02
DOI
https://doi.org/10.1021/acsnano.5c17087
Primary Topic
Evolutionary Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Technology Roadmap of Bioinspired Computing Hardware

Sarbajit Banerjee, Ioannis Messaris, Keqin Liu, W. Maass et al.
ACS Nano
Evolutionary Algorithms and Applications
article

Technology Roadmap of Bioinspired Computing Hardware

Sarbajit Banerjee, Ioannis Messaris, Keqin Liu, W. Maass, Tie Jun Cui, Saptarshi Das, Fernando Corinto, Matteo Farronato, Ilia Valov, Cheol Seong Hwang, Yasai Wang, John D. Ponis, F J Miao, Qinqi Ren, Qinghui Shao, Yoon Ho Jang, Piergiulio Mannocci, Michele Bonnin, Daniele Ielmini, Ming-Jay Yang, Marco Gilli, Emanuele Gemo, Stefan Slesazeck, Zhigang Wu, Max C. Lemme, Yoeri van de Burgt, Wei Zhang, Zheshun Xiong, Chai Yang, Kai Xiao, Jin Luo, Shengbo Wang, Yijia Fan, Pier Paolo Civalleri, Tengji Xu, K. Kian Ang, Yuchao Yang, Shaojie Liu, R. Tetzlaff, Yuekun Yang, Le Phuong Lan Tran, Leon Chua, Ming Deng, Qiangfei Xia, Dimitris Prousalis, Lu Fang, Vasilis Ntinas, Ahmet Samil Demirkol, Chaoran Huang, Yuhui He, Bojun Cheng, Alon Ascoli, John Paul Strachan, Benshan Wang, Anthony Vorias, Hongwei Tan, Shuang Wang, Jianshi Tang, Thomas Mikolajick, Qian Ma, Zdenka Kuncic, Shi-Jun Liang, Zhiyuan Li, Zi Rui Feng, Xiaozhe Wang, Sreetosh Goswami, Can Li
article en

Abstract

The rapid growth of artificial intelligence (AI) is increasingly constrained by fundamental hardware bottlenecks in computation throughput and energy efficiency. Bioinspired computing (BIC) offers a promising alternative by emulating the intrinsic advantages of biological systems, such as parallelism, adaptability, and robustness. Progress in BIC hardware demands interdisciplinary convergence that bridges materials science and device physics with neuroscience, computer science, mathematics, and information science. Therefore, the development of this cross-disciplinary field urgently requires a comprehensive roadmap that analyzes systematically and in-depth the frontier issues and the latest progress. In this roadmap, we categorize the critical challenges into three components: hardware foundations, architectures, and prototype realizations. We highlight how biological features inspire the design of BIC hardware through device physics and discuss their performance metrics and engineering challenges. We then describe how diverse signaling rules and structural organizations in BIC architectures support specific computational prototypes, including electronic and photonic BIC chips, and present a technological roadmap that outlines opportunities to expand the functional scope of BIC hardware through coordinated advances in devices, architectures, and system demonstrations. This ongoing convergence of interdisciplinary knowledge can help accelerate the shift toward high-efficiency AI hardware.

ACS NanoVol. 20(10)
ON Semiconductor (United States) (US), The University of Sydney (AU), Pennsylvania State University (US), Seoul National University (KR), Hong Kong Polytechnic University (HK), Shenzhen Institute of Information Technology (CN), National University of Singapore (SG), Forschungszentrum Jülich (DE), Politecnico di Torino (IT), Chinese University of Hong Kong (HK), Yonsei University (KR), Hong Kong University of Science and Technology (HK), Peking University (CN), Bulgarian Academy of Sciences (BG), Battery Park (US), Hong Kong Science and Technology Parks Corporation (HK), Paul Scherrer Institute (CH), Southern University of Science and Technology (CN), ETH Zurich (CH), Graz University of Technology (AT), Integrated Oncology (United States) (US), Max Planck Institute for Polymer Research (DE), Center for Excellence in Brain Science and Intelligence Technology (CN), Institute of Electrochemistry and Energy Systems (BG), Integrated Sensors (United States) (US), Grunberger Diabetes Institute (US), AMO (Germany) (DE), Institute for Biomedicine (IT), Institute of Microelectronics (CN), Machine Science (US), NaMLab (Germany) (DE), Fraunhofer Institute for Integrated Circuits (DE), MWT Materials (United States) (US), Division of Materials Science and Engineering (AU), Jülich Aachen Research Alliance (DE), Collaborative Innovation Center of Advanced Microstructures (CN), Consorzio di Bioingegneria e Informatica Medica (IT), Cloud Computing Center (CN), Institute for Complex Systems (IT), Shanghai Center for Brain Science and Brain-Inspired Technology (CN), Artificial Intelligence in Medicine (Canada) (CA), State Key Laboratory of Millimeter Waves, Huazhong University of Science and Technology (CN), Indian Institute of Science Bangalore (IN), United Microelectronics (United States) (US), Technische Universität Dresden (DE), Eindhoven University of Technology (NL), Xi'an Jiaotong University (CN), Nanjing University (CN), RWTH Aachen University (DE), University of Hong Kong (HK), Atlas Elektronik (United Kingdom) (GB), Politecnico di Milano (IT), University of California, Berkeley (US), University of Edinburgh (GB), Tsinghua University (CN), Aalto University (FI)
Openalex Percentile: Top 42%
Evolutionary Algorithms and Applications
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