EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link: https://ebookbell.com/faq

We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.

For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Neuroinspired Computing Using Resistive Synaptic Devices Shimeng Yu

Name: Neuroinspired Computing Using Resistive Synaptic Devices Shimeng Yu
SKU: BELL-5870138
Price: 31.00 USD
Availability: InStock
Rating: 5 (80 reviews)

SKU: BELL-5870138

$ 31.00 ~~$ 45.00~~ (-31%)

5.0

80 reviews

Neuroinspired Computing Using Resistive Synaptic Devices Shimeng Yu instant download after payment.

Publisher: Springer

File Extension: PDF

File size: 15.56 MB

Pages: 267

Author: Shimeng Yu

ISBN: 9783319543123, 9783319543130, 3319543121, 331954313X

Language: English

Year: 2017

Product desciption

Neuroinspired Computing Using Resistive Synaptic Devices Shimeng Yu by Shimeng Yu 9783319543123, 9783319543130, 3319543121, 331954313X instant download after payment.

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.