Neuro-memristive Circuits for Edge Computing: A review

Abstract

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 neuro-memristive circuits for edge computing.

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• Harnessing adaptive dynamics in neuro-memristive nanowire networks for transfer learning
• TMS-Crossbars with Tactile Sensing
• Analysis on reservoir activation with the nonlinearity harnessed from solution-processed molybdenum disulfide
• Hybrid CMOS/Memristor Circuit Design Methodology
• Exploring Spiking Neural Networks for Binary Classification in Multivariate Time Series at the Edge
• Memristive, Spintronic, and 2D-Materials-Based Devices to Improve and Complement Computing Hardware
• SOT-MRAM based Sigmoidal Neuron for Neuromorphic Architectures
• AutoGMap: Learning to Map Large-scale Sparse Graphs on Memristive Crossbars
• A Single-Cycle MLP Classifier Using Analog MRAM-based Neurons and Synapses
• Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI