Abstract
In the intelligent Internet-of-things (iIoT) systems, a large number of smart sensors will be involved in the whole system to generate useful or user-specific information. For the common sensors without intelligent processing ability, they will produce massive data to be transferred to the cloud system for further intelligent processing, which puts forward extremely high requirements on the processing capacity of cloud systems. As a promising solution, smart sensors can transfer the processing ability from the cloud to the sensor end, which means that large power consumption for the massive data transmission could be saved. However, due to the limitations of the hardware resources and “always-on” operating mode for the smart sensors, conventional architectures are difficult to meet the extremely high requirements for energy efficiency in continuous perception applications. Approximate computing would be a promising technology to balance the computing accuracy with energy efficiency and hardware cost for such continuous perception system. In fact, both in analog and digital domain, approximate computing could achieve large performance improvements or reduce the power consumption of the system in different levels.
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Liu, Z., Li, Q., Yang, X., Qiao, F. (2022). Cross-Level Design of Approximate Computing for Continuous Perception System. In: Liu, W., Lombardi, F. (eds) Approximate Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-98347-5_20
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DOI: https://doi.org/10.1007/978-3-030-98347-5_20
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