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Edge computing solutions that enable the extraction of high-level information from a variety of sensors is in increasingly high demand.
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J. Zhang, L. Feng, T. Wang, W. Shi, Y. Wang, and G. Zhang, “FPGA-Based Implementation of an Event-Driven Spiking Multi-Kernel Convolution Architecture,” IEEE Transactions on Circuits and Systems II: Express Briefs , vol. 69, no. 3, pp. 1682–1686, Mar. 2022. [Online]. Available: https://ieeexplore.ieee.org/document/9606220/
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2022
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2023
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O. Richter, C. Wu, A. M. Whatley, G. Köstinger, C. Nielsen, N. Qiao, and G. Indiveri, “DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor,” Neuromorphic Computing and Engineering , vol. 4, Jan. 2024. [Online]. Available: https://iopscience.iop.org/article/10.1088/2634-4386/ad1cd7
2024
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M. Yao, O. Richter, G. Zhao, N. Qiao, Y. Xing, D. Wang, T. Hu, W. Fang, T. Demirci, M. De Marchi, L. Deng, T. Yan, C. Nielsen, S. Sheik, C. Wu, Y. Tian, B. Xu, and G. Li, “Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip,” Nature Communications , vol. 15, no. 1, p. 4464, May 2024. [Online]. Available: https://www.nature.com/articles/s41467-024-47811-6
2024
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