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Titlebook: Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices; Manan Suri Book 2017 Springer (India) Pvt. Ltd. 2017 Low-power Co

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https://doi.org/10.1007/BFb0060998atility, high write and read speed, and outstanding endurance. The basic cell of STT-MRAM, the spin-transfer torque magnetic tunnel junction (STT-MTJ), is a resistive memory that can be switched by electrical current. STT-MTJs are nevertheless usually not considered as memristors as they feature onl
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Yves Guivarc’h,Lizhen Ji,J. C. Taylordedicated central processor. With the end of Dennard scaling and the resulting slowdown in Moore’s law, the IT industry is turning its attention to non-Von Neumann (non-VN) architectures, and in particular, to computing architectures motivated by the human brain. One family of such non-VN computing
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Mathematics: Theory & ApplicationsRAM [.] for synaptic emulation in dedicated neuromorphic hardware. Most of these works justify the use of RRAM devices in hybrid learning hardware on grounds of their inherent advantages, such as ultra-high density, high endurance, high retention, CMOS compatibility, possibility of 3D integration, a
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https://doi.org/10.1007/0-8176-4466-0al spike-timing-dependent plasticity (STDP) learning rule and can allow the design of learning systems. Such systems can be built with memristive devices of extremely diverse physics and behaviors and are particularly robust to device variations and imperfections. The present work investigates the t
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Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices978-81-322-3703-7Series ISSN 1867-4925 Series E-ISSN 1867-4933
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https://doi.org/10.1007/BFb0060998In this chapter, theory, circuit design methodologies and possible applications of Cellular Nanoscale Networks (CNNs) exploiting memristor technology are reviewed. Memristor-based CNNs platforms (MCNNs) make use of memristors to realize analog multiplication circuits that are essential to perform CNN calculation with low power and small area.
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