Named data networking with neural networks for intelligent image processing information systems

Abstract

Rapid retrieval of differentiated name data and efficient storage of information is essential in intelligent image processing information systems. In order to meet the requirements and challenges of rapid retrieval of differentiated name data and efficient storage of image information under different application scenarios of pending interest table (PIT) of named data network, a learning PIT overall scheme is proposed. This concept is known as the Learning Tree PIT (LT-PIT), which is based on a neural network framework. The index structure of LT-PIT, improves the image information storage efficiency by learning the distribution of index content in memory. The experimental results show that the comprehensive performance of LT-PIT is better than other schemes. When the number of names reaches 2 million, the index structure memory consumption of LT-PIT is 253.129 MB, including 53.129 MB of on-chip memory consumption, which can be deployed on high-speed memory SRAM. This technique is applicable to intelligent image processing information systems and can be used to process big data of images in the future.