Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices
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Beschreibung
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 book offers readers a bidirectional (top-down and bottom-up) perspective on designing efficient bio-inspired hardware. At the nanodevice level, it focuses on various flavors of emerging resistive memory (RRAM) technology. At the algorithm level, it addresses optimized implementations of supervised and stochastic learning paradigms such as: spike-time-dependent plasticity (STDP), long-term potentiation (LTP), long-term depression (LTD), extreme learning machines (ELM) and early adoptions of restricted Boltzmann machines (RBM) to name a few. The contributions discuss system-level power/energy/parasitic trade-offs, and complex real-world applications. The book is suited for both advanced researchers and students interested in the field.
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 book offers readers a bidirectional (top-down and bottom-up) perspective on designing efficient bio-inspired hardware. At the nanodevice level, it focuses on various flavors of emerging resistive memory (RRAM) technology. At the algorithm level, it addresses optimized implementations of supervised and stochastic learning paradigms such as: spike-time-dependent plasticity (STDP), long-term potentiation (LTP), long-term depression (LTD), extreme learning machines (ELM) and early adoptions of restricted Boltzmann machines (RBM) to name a few. The contributions discuss system-level power/energy/parasitic trade-offs, and complex real-world applications. The book is suited for both advanced researchers and students interested in the field.
Employs a flexible format, so that it can be used either (a) as a regular graduate level course textbook or (b) as an advanced research manuscript in the field of neuromorphic hardware Places major emphasis on experimental results, fabricated device technology aspects and not just modeling/simulation-based results Offers highly relevant and interdisciplinary content, uniting the fields of nanoelectronics (devices/materials), computational neuroscience (learning rules/algorithms), and computing (architectures/applications) Includes supplementary material: sn.pub/extras
Autor*in
Manan Suri
Themen in »Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices«
Low-power Cognitive Hardware Memristor Neuromorphic Hardware Resistive Memory Technology Supervised and Unsupervised Learning