TY  - GEN
N2  - 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.
DO  - 10.1007/978-81-322-3703-7
DO  - doi
AB  - 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.
T1  - Advances in neuromorphic hardware exploiting emerging nanoscale devices /
DA  - 2017.
CY  - New Delhi :
AU  - Suri, Manan.
VL  - v. 31
CN  - QA76.87
CN  - TA1-2040
PB  - Springer,
PP  - New Delhi :
PY  - 2017.
N1  - Novel Biomimetic Si Devices for Neuromorphic Computing Architecture.
ID  - 806736
KW  - Neural networks (Computer science)
KW  - Computer architecture.
KW  - Analog CMOS integrated circuits.
SN  - 9788132237037
SN  - 813223703X
TI  - Advances in neuromorphic hardware exploiting emerging nanoscale devices /
LK  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-81-322-3703-7
UR  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-81-322-3703-7
ER  -