518 research outputs found
Neuro-memristive Circuits for Edge Computing: A review
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
Memristor Crossbar-based Hardware Implementation of IDS Method
Ink Drop Spread (IDS) is the engine of Active Learning Method (ALM), which is
the methodology of soft computing. IDS, as a pattern-based processing unit,
extracts useful information from a system subjected to modeling. In spite of
its excellent potential in solving problems such as classification and modeling
compared to other soft computing tools, finding its simple and fast hardware
implementation is still a challenge. This paper describes a new hardware
implementation of IDS method based on the memristor crossbar structure. In
addition of simplicity, being completely real-time, having low latency and the
ability to continue working after the occurrence of power breakdown are some of
the advantages of our proposed circuit.Comment: 16 pages, 13 figures, Submitted to IEEE Transaction on Fuzzy System
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