DRAMSys4.0: An Open-Source Simulation Framework for In-depth DRAM Analyses

Author:

Steiner LukasORCID,Jung Matthias,Prado Felipe S.,Bykov Kirill,Wehn Norbert

Abstract

AbstractThe simulation of Dynamic Random Access Memories (DRAMs) on system level requires highly accurate models due to their complex timing and power behavior. However, conventional cycle-accurate DRAM subsystem models often become a bottleneck for the overall simulation speed. A promising alternative are simulators based on Transaction Level Modeling, which can be fast and accurate at the same time. In this paper we present DRAMSys4.0, which is, to the best of our knowledge, the fastest and most extensive open-source cycle-accurate DRAM simulation framework. DRAMSys4.0 includes a novel software architecture that enables a fast adaption to different hardware controller implementations and new JEDEC standards. In addition, it already supports the latest standards DDR5 and LPDDR5. We explain how to apply optimization techniques for an increased simulation speed while maintaining full temporal accuracy. Furthermore, we demonstrate the simulator’s accuracy and analysis tools with two application examples. Finally, we provide a detailed investigation and comparison of the most prominent cycle-accurate open-source DRAM simulators with regard to their supported features, analysis capabilities and simulation speed.

Funder

Fraunhofer-Gesellschaft

Deutsche Forschungsgemeinschaft

Technische Universität Kaiserslautern

Publisher

Springer Science and Business Media LLC

Subject

Information Systems,Theoretical Computer Science,Software

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. CRISP: Triangle Counting Acceleration via Content Addressable Memory-Integrated 3D-Stacked Memory;2024 IEEE International Test Conference in Asia (ITC-Asia);2024-08-18

2. MEPAD: A Memory-Efficient Parallelized Direct Convolution Algorithm for Deep Neural Networks;Lecture Notes in Computer Science;2024

3. Addressing DRAM Performance Analysis Challenges for Network-on-Chip (NoC) Design;Proceedings of the International Symposium on Memory Systems;2023-10-02

4. Deep Neural Network Memory Performance and Throughput Modeling and Simulation Framework;Mathematics;2022-11-06

5. GCIM: Toward Efficient Processing of Graph Convolutional Networks in 3D-Stacked Memory;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2022-11

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3