PIM-IoT: Enabling hierarchical, heterogeneous, and agile Processing-in-Memory in IoT systems

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-02-28 DOI:10.1016/j.future.2025.107782

Kan Zhong , Qiao Li , Ao Ren , Yujuan Tan , Xianzhang Chen , Linbo Long , Duo Liu

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引用次数: 0

Abstract

The Internet of Things (IoT) is an emerging concept that senses the physical world by connecting various “things” and objects to the Internet. Conventional cloud-based IoT systems are unlikely to keep up with the diverse needs of IoT applications and have some issues, such as privacy and latency. Edge computing based IoT systems solve these issues by placing data processing and inference tasks near the data source. However, due to the increasing complexity of IoT applications, performing data processing and inference tasks in edge computing based IoT systems can lead to high energy consumption and latency.

Processing-in-Memory (PIM) is a promising solution to reduce the energy consumption of data processing and inference tasks by closely integrating computational logics with memory device. Therefore, in this paper, we propose PIM-IoT, a PIM architectures enabled IoT system to reduce the energy consumption. To accommodate various data processing tasks, we architect PIM-IoT as a hierarchical system that consists of 3 tiers: sensing tier, gateway tier, and edge computing tier. We first analyze the dataflow of typical IoT applications and map tasks to different tiers. To handle the data processing and inference tasks effectively in each tier, we then propose hierarchical, heterogeneous, and collaborative PIM architectures for each tier. Finally, we show how multi-tier can be co-optimized under latency and power constraints. To our knowledge, this is the first work to explore novel PIM architectures in IoT systems. Detailed analysis and experimental results show that PIM-IoT can achieve 5.6x performance improvement and 6x energy consumption reduction for IoT applications.

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来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.