Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 31, Issue 1, Pages 4-23Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2019.2899262
Keywords
Computer architecture; Edge computing; Neuromorphics; Hardware; Memristors; Cloud computing; Data processing; Cellular neural network (CeNN); convolutional neural network (CNN); deep learning neural network; hierarchical temporal memory (HTM); long short-term memory (LSTM); memristor circuits; memristors; neural networks; spiking neural networks (SNNs)
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The volume, veracity, variability, and velocity of data produced from the ever increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks, and open problems in the field of neuromemristive circuits for edge computing.
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