Performance Evaluation of NoC-Based Multicore Systems

Author:

Qian Zhiliang1,Bogdan Paul2,Tsui Chi-Ying3,Marculescu Radu4

Affiliation:

1. Shanghai Jiao Tong University, Shanghai, China

2. University of Southern California, Los Angeles, CA

3. The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

4. Carnegie Mellon University, Pittsburgh, PA

Abstract

In this survey, we review several approaches for predicting performance of Network-on-Chip (NoC)-based multicore systems, starting from the traffic models to the complex NoC models for latency evaluation. We first review typical traffic models to represent the application workloads in NoC. Specifically, we review Markovian and non-Markovian (e.g., self-similar or long-range memory-dependent) traffic models and discuss their applications on multicore platform design. Then, we review the analytical techniques to predict NoC performance under given input traffic. We investigate analytical models for average as well as maximum delay evaluation. We also review the developments and design challenges of NoC simulators. One interesting research direction in NoC performance evaluation consists of combining simulation and analytical models in order to exploit their advantages together. Toward this end, we discuss several newly proposed approaches that use hardware-based or learning-based techniques. Finally, we summarize several open problems and our perspective to address these challenges.

Funder

US National Science Foundation

Hong Kong Research Grants Council

Publisher

Association for Computing Machinery (ACM)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications

Reference142 articles.

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2. TOPAZ: An Open-Source Interconnection Network Simulator for Chip Multiprocessors and Supercomputers

3. ESESC: A fast multicore simulator using Time-Based Sampling

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1. Fast Analysis Using Finite Queuing Model for Multilayer NoCs;IEEE Design & Test;2023-12

2. Fast and Accurate NoC Latency Estimation for Application-Specific Traffics via Machine Learning;IEEE Transactions on Circuits and Systems II: Express Briefs;2023-09

3. An Artificial Bee Colony Based Mapping Method for Three Dimensional Network-on-Chip;2023 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC);2023-08-16

4. Fast NoC Router Latency Estimation Using Machine Learning;2023 China Semiconductor Technology International Conference (CSTIC);2023-06-26

5. Non-Gaussian Traffic Modeling for Multicore Architecture Using Wavelet Based Rosenblatt Process;IEEE Access;2023

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