93 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
CMOS and memristive hardware for neuromorphic computing
The ever-increasing processing power demands of digital computers cannot continue to be fulfilled indefinitely unless there is a paradigm shift in computing. Neuromorphic computing, which takes inspiration from the highly parallel, low power, high speed, and noise-tolerant computing capabilities of the brain, may provide such a shift. To that end, various aspects of the brain, from its basic building blocks, such as neurons and synapses, to its massively parallel in-memory computing networks have been being studied by the huge neuroscience community. Concurrently, many researchers from across academia and industry have been studying materials, devices, circuits, and systems, to implement some of the functions of networks of neurons and synapses to develop bio-inspired (neuromorphic) computing platforms
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