Sigmoid Activation Implementation for Neural Networks Hardware Accelerators Based on Reconfigurable Computing Environments for Low-Power Intelligent Systems

Sigmoid Activation Implementation for Neural Networks Hardware Accelerators Based on Reconfigurable Computing Environments for Low-Power Intelligent Systems

The remarkable results of applying machine learning algorithms to complex tasks are well known. They open wide opportunities in natural language processing, image recognition, and predictive analysis. However, their use in low-power intelligent systems is restricted because of high computational com...

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Bibliographic Details
Main Authors: Vladislav Shatravin, Dmitriy Shashev, Stanislav Shidlovskiy
Format: Article
Language:English
Published: MDPI AG 2022-05-01
Series:Applied Sciences
Subjects:
deep neural networks
hardware accelerators
low-power systems
homogeneous structures
reconfigurable environments
parallel processing
Online Access:https://www.mdpi.com/2076-3417/12/10/5216

Internet

https://www.mdpi.com/2076-3417/12/10/5216

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