Powered by OpenAIRE graph
Found an issue? Give us feedback
addClaim

Understanding the Architectural Characteristics of EDA Algorithms

Authors: Xin Wang; Xiaofeng Ji; Yunping Lu; Yi Li; Weijia Zhou; Weihua Zhang; Wenyun Zhao;

Understanding the Architectural Characteristics of EDA Algorithms

Abstract

Currently, the release of different chip products has come to a burst. Time-to-market period of these products has been shortened to an extreme, nearly 8 to 12 months. To reduce production period, hardware architects try to shorten every design and manufacture stage. Therefore, it has become one of the major concerns for them that how to accelerate electronic design automation (EDA) tools, which have been widely used throughout the lifetime of chip design and manufacture. While many prior efforts have done in-depth works on different acceleration techniques, such as IC-based, FPGA-based, or GPUbased, to our best knowledge, there has been no systematic study towards the architectural characteristics analysis for these EDA algorithms. This may impede the further optimizations and acceleration for them. In this paper, we make the first attempt to construct an EDA benchmark suite (EDAbench for short) for architectural design, parallel acceleration, and system optimization. EDAbench covers representative modern EDA algorithms. We then evaluate predominant architectural characteristics from three aspects including computation characteristics, memory hierarchy, and systematic characteristics. Experimental results reveal that there are some vital gaps between existing hardware and the requirements of EDA algorithms. Based on the analysis, we also give out some insights and propose suggestions for future optimization, acceleration, and architecture design.

Related Organizations
  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!