Accelerating PageRank Algorithmic Tasks with a new Programmable Hardware Architecture

TL;DR

This paper introduces a reconfigurable hardware accelerator optimized for AI and data analytics, demonstrating significant speedups in PageRank computations on complex networks.

Contribution

It presents a novel programmable hardware architecture with messaging-based computing for efficient AI and data analytics tasks, validated through a case study on protein network analysis.

Findings

Analyzed a 5,000-node protein network in 213.6 ms

Achieved high performance in PageRank computations

Validated design through simulation in 28nm technology

Abstract

Addressing the growing demands of artificial intelligence (AI) and data analytics requires new computing approaches. In this paper, we propose a reconfigurable hardware accelerator designed specifically for AI and data-intensive applications. Our architecture features a messaging-based intelligent computing scheme that allows for dynamic programming at runtime using a minimal instruction set. To assess our hardware's effectiveness, we conducted a case study in TSMC 28nm technology node. The simulation-based study involved analyzing a protein network using the computationally demanding PageRank algorithm. The results demonstrate that our hardware can analyze a 5,000-node protein network in just 213.6 milliseconds over 100 iterations. These outcomes signify the potential of our design to achieve cutting-edge performance in next-generation AI applications.