Shuang Wang, Zhiyuan Li, Mengjiao Pei, Qinqi Ren, Ming Deng, K. Kian 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 C. Lemme, Can Li, Shi-Jun Liang, Keqin Liu, Shaojie Liu, Jin Luo, Qian Ma, W. Maass, Piergiulio Mannocci, Ioannis Messaris, F J Miao, Thomas Mikolajick, Vasilis Ntinas, John D. 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, Chai Yang
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.