Neuro-inspired Computing Using Resistive Synaptic Devices -
-25% su kodu BOOKS
Pristatymas per 15-21 d.d.
30 dienų grąžinimo politika
This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integr ... Visas aprašymas
Jums taip pat gali patikti
Aprašymas
This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration. This book also discusses peripheral neuron circuits design challenges and design strategies. Finally, the authors describe the impact of device non-ideal properties (e.g. noise, variation, yield) and their impact on the learning performance at the system-level, using a device-algorithm co-design methodology.
Daugiau informacijos
| Leidėjas | Springer Nature Switzerland |
|---|---|
| Išleidimo metai | 2018 |
| Viršelio tipas | Minkšti viršeliai |
| EAN | 9783319853680 |