DataVXACS NanoPaper
journal article·2026-03-17Open access

Technology Roadmap of Bioinspired Computing Hardware

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

ACS Nano· Volume 20· Issue 10
Publisher pageOpen full text
Source abstract

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.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Evolutionary Algorithms and Applications · Physical Unclonable Functions (PUFs) and Hardware Security · Embedded Systems Design Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Applied Physics

The Hong Kong Polytechnic University

Joint Research Centre of Microelectronics

Research Institute for Artificial Intelligence of Things

Department of Electrical and Computer Engineering

National University of Singapore

Department of Electronics and Telecommunications

Politecnico di Torino

Laboratory for Inorganic Chemistry, Department of Chemistry and Applied Biosciences

ETH Zürich

Laboratory for Battery Science, PSI Center for Energy and Environmental Sciences

Paul Scherrer Institute

Department of Mechanical Engineering

Eindhoven University of Technology

Institute for Complex Molecular Systems

Microelectronics Thrust

The Hong Kong University of Science and Technology (Guangzhou)

Department of Electrical Engineering and Computer Sciences

University of California, Berkeley

State Key Laboratory of Millimeter Waves

Southeast University

Engineering Science and Mechanics

The Pennsylvania State University

Institut für Grundlagen der Elektrotechnik und Elektronik

Technische Universität Dresden

Funding

National Key Research and Development Program of China · Division of Emerging Frontiers and Multidisciplinary Activities · Science, Technology and Innovation Commission of Shenzhen Municipality · Defence Research and Development Organisation · National Natural Science Foundation of China · Research Grants Council, University Grants Committee · Ministry of Electronics and Information technology · Science and Engineering Research Board · Basic Energy Sciences · Austrian Science Fund · Deutsche Forschungsgemeinschaft · Natural Science Foundation of Jiangsu Province · Bundesministerium für Forschung und Technologie · Innovation and Technology Commission · Max-Planck-Gesellschaft · Ministry of Education, India · Croucher Foundation · Research Council of Finland · Natural Science Foundation of Beijing Municipality · Chips Joint Undertaking · Tsinghua University · Hong Kong Polytechnic University · HORIZON EUROPE Digital, Industry and Space · Guangdong Provincial Key Laboratory of In-Memory Computing Chips

References (100+)

Show source-provided references
1. Amodei, D.; Hernandez, D. AI and compute ; OpenAI Blog. 2018. https://openai.com/index/ai-and-compute/.
2. 10.1038/s41586-021-04362-w10.1038/s41586-021-04362-w ↗
3. 10.1038/nature1453910.1038/nature14539 ↗
4. 10.1109/MCSE.2017.3110.1109/MCSE.2017.31 ↗
5. 10.1038/d41586-025-00616-z10.1038/d41586-025-00616-z ↗
6. 10.48550/arXiv.2104.1035010.48550/arXiv.2104.10350 ↗
7. 10.1038/503022a10.1038/503022a ↗
8. Von Neumann J. · 2012
9. 10.1038/s41928-017-0006-810.1038/s41928-017-0006-8 ↗
10. 10.1109/JPROC.2015.244409410.1109/JPROC.2015.2444094 ↗
11. 10.1063/5.007879810.1063/5.0078798 ↗
12. 10.1109/JPROC.2014.231395410.1109/JPROC.2014.2313954 ↗
13. 10.48550/arXiv.2408.1481110.48550/arXiv.2408.14811 ↗
14. 10.1038/s41928-021-00646-110.1038/s41928-021-00646-1 ↗
15. Buzsáki G. · 201910.1093/oso/9780190905385.001.0001 ↗
16. 10.1093/nsr/nwae14410.1093/nsr/nwae144 ↗
17. Kandel E. R. · 2000
18. 10.1016/S0928-4257(00).01090-110.1016/S0928-4257(00).01090-1 ↗
19. 10.1038/s41565-024-01665-710.1038/s41565-024-01665-7 ↗
20. 10.1038/s41928-020-00501-910.1038/s41928-020-00501-9 ↗
21. 10.3390/cells1012326910.3390/cells10123269 ↗
22. Kolb H. · Am. Sci. · 2003 · 91 · 2810.1511/2003.11.28 ↗
23. 10.1016/j.cub.2006.05.05610.1016/j.cub.2006.05.056 ↗
24. Rieke F. · 1999
25. 10.1016/j.pneurobio.2011.08.00210.1016/j.pneurobio.2011.08.002 ↗
26. 10.3389/fnins.2011.0007310.3389/fnins.2011.00073 ↗
27. 10.1038/nnano.2012.24010.1038/nnano.2012.240 ↗
28. 10.1038/s41928-018-0103-310.1038/s41928-018-0103-3 ↗
29. 10.1038/s42254-020-0208-210.1038/s42254-020-0208-2 ↗
30. 10.1088/2634-4386/ac4a8310.1088/2634-4386/ac4a83 ↗
31. 10.1038/s41566-020-00754-y10.1038/s41566-020-00754-y ↗
32. 10.1038/s43588-021-00184-y10.1038/s43588-021-00184-y ↗
33. 10.1109/JPROC.2024.342936010.1109/JPROC.2024.3429360 ↗
34. 10.1038/s41586-024-08253-810.1038/s41586-024-08253-8 ↗
35. 10.1162/neco_a_0155310.1162/neco_a_01553 ↗
36. 10.1038/nature0693210.1038/nature06932 ↗
37. 10.1021/nl904092h10.1021/nl904092h ↗
38. 10.1038/nmat351010.1038/nmat3510 ↗
39. 10.1038/nature1444110.1038/nature14441 ↗
40. 10.1038/s41586-019-1157-810.1038/s41586-019-1157-8 ↗
41. 10.1038/s41586-020-2038-x10.1038/s41586-020-2038-x ↗
42. 10.1038/scientificamerican0591-7610.1038/scientificamerican0591-76 ↗
43. 10.1109/IJCNN.2008.463382810.1109/IJCNN.2008.4633828 ↗
44. 10.1109/TC.2012.14210.1109/TC.2012.142 ↗
45. 10.1126/science.125464210.1126/science.1254642 ↗
46. 10.1109/MM.2018.11213035910.1109/MM.2018.112130359 ↗
47. 10.1038/s41566-021-00796-w10.1038/s41566-021-00796-w ↗
48. 10.1093/nsr/nwae10210.1093/nsr/nwae102 ↗
49. 10.48550/arXiv.2401.0449110.48550/arXiv.2401.04491 ↗
50. 10.1007/BF0247825910.1007/BF02478259 ↗
51. Hebb D. O. · 1949
52. 10.1037/h004251910.1037/h0042519 ↗
53. Widrow B. · 1960
54. 10.1109/PROC.1976.1009210.1109/PROC.1976.10092 ↗
55. 10.1364/OL.2.00000110.1364/OL.2.000001 ↗
56. Rinzel J. · 1987 · 26710.1007/978-3-642-93360-8_26 ↗
57. McCulloch W. S. · Bull. Math. Biol. · 1990 · 52 · 9910.1016/S0092-8240(05)80006-0 ↗
58. 10.1523/JNEUROSCI.02-01-00032.198210.1523/JNEUROSCI.02-01-00032.1982 ↗
59. 10.1073/pnas.79.8.255410.1073/pnas.79.8.2554 ↗
60. Holler · 1990
61. Raffel J. I. · Lincoln Lab. J. · 1989 · 2
62. 10.1109/5.5835610.1109/5.58356 ↗
63. Mead C. A. · 1989 · 1
64. 10.1109/4.13315610.1109/4.133156 ↗
65. 10.1038/354515a010.1038/354515a0 ↗
66. 10.1109/72.21719310.1109/72.217193 ↗
67. 10.1109/72.10541910.1109/72.105419 ↗
68. Swaroop B. · 1998 · B3310.1109/ISCAS.1998.703888 ↗
69. 10.1162/neco.1997.9.8.173510.1162/neco.1997.9.8.1735 ↗
70. Sivilotti, M. A. Wiring considerations in analog VLSI systems, with application to field-programmable networks ; Doctor of Philosophy, California Institute of Technology, 1992.
71. Mahowald, M. VLSI analogs of neuronal visual processing: A synthesis of form and function ; Doctor of Philosophy, California Institute of Technology, 1992.
72. Mahowald M. A. · 199410.1007/978-1-4615-2724-4 ↗
73. 10.1109/82.84211010.1109/82.842110 ↗
74. 10.1109/16.54303510.1109/16.543035 ↗
75. 10.1162/08997660030001491710.1162/089976600300014917 ↗
76. 10.1162/08997660030001475510.1162/089976600300014755 ↗
77. 10.1109/4.9201510.1109/4.92015 ↗
78. 10.1109/ISCAS.2003.120634210.1109/ISCAS.2003.1206342 ↗
79. 10.1109/TNN.2006.88300710.1109/TNN.2006.883007 ↗
80. 10.1038/nmat200910.1038/nmat2009 ↗
81. 10.1002/adfm.20120238310.1002/adfm.201202383 ↗
82. 10.1021/nl901874j10.1021/nl901874j ↗
83. 10.1038/nnano.2008.16010.1038/nnano.2008.160 ↗
84. 10.1088/0957-4484/20/42/42520410.1088/0957-4484/20/42/425204 ↗
85. 10.1143/JJAP.30.L21510.1143/JJAP.30.L215 ↗
86. 10.1109/16.53681510.1109/16.536815 ↗
87. 10.1021/nl025532n10.1021/nl025532n ↗
88. 10.1038/nmat126910.1038/nmat1269 ↗
89. 10.1063/1.303339910.1063/1.3033399 ↗
90. 10.1109/iembs.2009.533325110.1109/iembs.2009.5333251 ↗
91. 10.1109/72.91453210.1109/72.914532 ↗
92. 10.1109/TNN.2003.81636710.1109/TNN.2003.816367 ↗
93. Vogelstein R. J. · 2004 · 385
94. 10.1109/TNN.2005.86085010.1109/TNN.2005.860850 ↗
95. 10.1038/nmat274810.1038/nmat2748 ↗
96. 10.1063/1.351851410.1063/1.3518514 ↗
97. 10.1063/1.339915210.1063/1.3399152 ↗
98. 10.1021/nn202983n10.1021/nn202983n ↗
99. 10.1038/nmat305410.1038/nmat3054 ↗
100. Schemmel J. · 2010 · 194710.1109/ISCAS.2010.5536970 ↗