Abstract
The proliferation of AI into everyday devices is a major trend today. This trend combined with the increasing amount of different AI hardware architectures and software frameworks imposes significant challenges when we want to interconnect such AI-based devices into single, large AI-driven distributed system. This paper addresses one key challenge which is around the problem of sharing AI encoded information among components of vastly heterogeneous nature. For that end we propose a new concept called Neuromorphic Data Layer, which can bridge various internal AI data representations in a communication channel-friendly way. The proposed methods are also stress tested in a distributed industrial robotic control & training use-case where all components are state-of-the-art devices, have some form of AI computation and they are interconnected over wireless technologies using the proposed Neuromorphic Data Layer.
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Acknowledgements
The authors would like to thank the INRC [17] making us the Loihi cloud and the Kapoho Bay device available.
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Veres, A., Hága, P., Rácz, A., Borsos, T., Kenesi, Z. (2022). A Communication Data Layer for Distributed Neuromorphic Systems. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 647. Springer, Cham. https://doi.org/10.1007/978-3-031-08337-2_1
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DOI: https://doi.org/10.1007/978-3-031-08337-2_1
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