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A Processing-in-Memory Architecture Programming Paradigm for Wireless Internet-of-Things Applications.

Xu Yang¹, Yumin Hou², Hu He³

¹School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China. yangxu@tsinghua.edu.cn.

Sensors (Basel, Switzerland)

|January 6, 2019

Summary

This summary is machine-generated.

Processing-in-Memory (PIM) offers a new computing paradigm for wireless Internet of Things (IoT) Big Data challenges. This approach reduces power consumption and execution time for IoT applications by processing data near memory.

Keywords:

Internet of Things Processing-in-Memory programming paradigm

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Area of Science:

Computer Science
Electrical Engineering
Data Science

Background:

Wireless Internet of Things (IoT) applications generate Big Data, posing challenges for traditional computing systems due to bandwidth and latency constraints.
Large-scale data movement significantly impacts performance, power efficiency, and reliability in computing systems.
There is a growing demand for novel computing paradigms to address the limitations of current systems in Big Data scenarios.

Purpose of the Study:

To propose a novel programming paradigm for Processing-in-Memory (PIM) architectures tailored for wireless IoT applications.
To introduce a data-transferring mechanism and middleware architecture to support the proposed PIM paradigm.
To demonstrate the validity and efficiency of the PIM approach for typical IoT applications.

Main Methods:

Development of a PIM programming paradigm and associated data-transferring mechanism and middleware.
Design and implementation of a simulation platform using Gem5.
Creation of a Field-Programmable Gate Array (FPGA) demonstration design for PIM architecture.
Evaluation using typical IoT applications like multimedia and MapReduce programs.

Main Results:

Successful execution of IoT applications on both the Gem5-based simulation platform and the FPGA demo.
Significant reduction in power consumption for the evaluated applications.
Substantial decrease in execution time for the evaluated applications.
Demonstrated efficiency and validity of the proposed PIM programming paradigm for wireless IoT.

Conclusions:

The proposed PIM programming paradigm effectively addresses the Big Data challenges in wireless IoT.
PIM architecture offers significant advantages in power efficiency and execution speed for IoT applications.
The developed simulation and FPGA platforms validate the practical applicability of the PIM approach.