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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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Compactifications of Symmetric Spacesic weight resolution and the robustness to device variability of the network have been investigated. Statistical evaluation of device variability is obtained on a 16 kbit OxRAM memory array integrated into advanced 28 nm CMOS technology.
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Yves Guivarc’h,Lizhen Ji,J. C. Taylornaptic behavior in densely packed crossbar arrays suitable for on-chip learning. We discuss our recent research investigating the characteristics needed from such nonvolatile memory elements for implementation of high-performance ANNs. We describe experiments on a 3-layer perceptron network with 164
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https://doi.org/10.1007/0-8176-4466-0r and complexity/area. Another challenge is realizing an area- and power-efficient implementation of the electronic neuron. A leaky integrate-and-fire (LIF) neuron has been implemented using analog and digital circuits which are highly power and area inefficient. To improve area and power efficiency
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https://doi.org/10.1007/0-8176-4466-0 the input. This theoretical analysis allows interpreting how STDP differs for several device physics and why it is robust to device mismatch. It can also provide guidelines for designing STDP-based learning systems.
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Hardware Spiking Artificial Neurons, Their Response Function, and Noises,Overview:
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1867-4925 rasitic trade-offs, and complex real-world applications. The book is suited for both advanced researchers and students interested in the field..978-81-322-3890-4978-81-322-3703-7Series ISSN 1867-4925 Series E-ISSN 1867-4933
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1867-4925 script in the field of neuromorphic hardware.Places major em.This book covers all major aspects of cutting-edge research in the field of neuromorphic hardware engineering involving emerging nanoscale devices. Special emphasis is given to leading works in hybrid low-power CMOS-Nanodevice design. The
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