Is the EDA Industry Ready for Cloud Computing?

Sehgal, Neeta; Acken, John M.; Sohoni, Sohum

doi:10.1080/02564602.2015.1099056

Cited by 9 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Simulation algorithms simulate the functionality and performance (e.g., timing, power and energy consumption, thermal properties, and so on) of an electronic design by approximating the electromagnetic and material properties of an IC. They are extensions and adaptations of general-purpose numerical methods, like Finite Element Methods and grid techniques to solve large second-order partial differential (Huang et al, 2009;Sehgal et al, 2016;Stojcev, 2006;Vladimirescu, 1994). Specification expresses the different facets of an electronic design using Hardware Description Languages, i.e., Verilog, VHLD and VHDL-AMS, to describe the functionality or structure or both of a design (Doboli et al, 1999;Doboli & Vemuri, 2002Huang et al, 2009).…”

Section: The Past and Present Of Edamentioning

confidence: 99%

“…Automated design (synthesis) algorithms transform and optimize a design across the abstraction hierarchy. It starts from the higher-levels of abstraction usually concerned with the procedural presentation of the desired functionality, followed by creating customized and optimized single-, multi-, custom-, and Intellectual Property (IP)-core based architectures and structures (Das et al, 2015;Sehgal et al, 2016;Subramanian et al, 2013;Yu et al, 2018), and finally devising the layouts of ICs and SoCs (Agnesina et al, 2020;Maxfield, 2008;Schaefer, 1981;Sherwani, 1999;Thepayasuwan & Doboli, 2004;Wang et al, 2009;Ward et al, 2012). A broad range of single-and multi-level (hierarchical) optimization techniques have been explored (Brayton & Cong, 2009;Li et al, 2016;Tang et al, 2006;Zuluaga et al, 2013).…”

Section: The Past and Present Of Edamentioning

confidence: 99%

See 1 more Smart Citation

Combining informetrics and trend analysis to understand past and current directions in electronic design automation

Curiac

Doboli

2022

Scientometrics

View full text Add to dashboard Cite

There has been increasing interest in the study of research communities with the goal of optimizing their outcomes and impact. While current methods can predict future trends, they offer little insight about the causes of the trends. However, causal insight is important for strategic decision making to improve a community. This paper presents a new method to predict the possible causes for inefficiencies in a community by relating them to disconnections between trends, like trends in the number of publications, patents, citations, and so on. The method combines traditional scientometric and webometric metrics and metric predictions with a recent model for trend analysis in a community. The proposed method was used to analyze electronic design automation (EDA) domain. The analysis showed intriguing disconnections between the trends of the number of papers, number of granted patents, and impact of its main publications. The analysis suggests a slightly decreasing impact and visibility of EDA, while having less novel, commonly-accepted knowledge in the area. The gained insight suggests three possible strategic decisions to improve EDA community: avoiding to ignore new ideas, reducing the complexity of framed problems, and keeping a minimal gap between real-life needs and academic solutions.

show abstract

Section: The Past and Present Of Edamentioning

confidence: 99%

Section: The Past and Present Of Edamentioning

confidence: 99%

Combining informetrics and trend analysis to understand past and current directions in electronic design automation

Curiac

Doboli

2022

Scientometrics

View full text Add to dashboard Cite

show abstract

“…cloud) to gain near-instant access to compute resources [1]. Migrating EDA jobs to the cloud has helped teams meet the demands of their tapeout schedule, hence reducing the time to market [2]. For example, horizontal scaling by launching more compute servers allows EDA teams to complete a highly-parallelizable compute job such as simulation in less time.…”

Section: Introductionmentioning

confidence: 99%

Characterizing and Optimizing EDA Flows for the Cloud

Hosny¹,

Reda²

2021

Preprint

View full text Add to dashboard Cite

Cloud computing accelerates design space exploration in logic synthesis, and parameter tuning in physical design. However, deploying EDA jobs on the cloud requires EDA teams to deeply understand the characteristics of their jobs in cloud environments. Unfortunately, there has been little to no public information on these characteristics. Thus, in this paper, we formulate the problem of migrating EDA jobs to the cloud. First, we characterize the performance of four main EDA applications, namely: synthesis, placement, routing and static timing analysis. We show that different EDA jobs require different machine configurations. Second, using observations from our characterization, we propose a novel model based on Graph Convolutional Networks to predict the total runtime of a given application on different machine configurations. Our model achieves a prediction accuracy of 87%. Third, we develop a new formulation for optimizing cloud deployments in order to reduce deployment costs while meeting deadline constraints. We present a pseudo-polynomial optimal solution using a multichoice knapsack mapping that reduces costs by 35.29%.

show abstract

Radio‐Frequency‐Integrated Circuits

Chongcheawchamnan,

Karacaoglu,

Amsdon

et al. 2024

Encyclopedia of RF and Microwave Engineering

View full text Add to dashboard Cite

This chapter gives an overview of the technology and design of radiofrequency (RF)‐integrated circuits in silicon technology. These circuits have transformed the field of wireless communications, forming the heart of sophisticated multiple‐input, multiple‐output RF transceivers for use in cellular handsets. The technology is now starting to transform new fields in the millimeter‐wave frequency range, enabling it to be used for new consumer applications such as radars for autonomous cars. The chapter describes the key active device technologies and passive components before going on to describe the state of the art in circuit design techniques. There are separate sections dedicated to the design of amplifiers, oscillators, mixers and modulators, and integrated transceivers. A final section gives an introduction to the importance of taking the packaging into account at the chip design and layout stage, as well as describing the rapidly emerging field of RF system‐in‐package technology, where sophisticated millimeter‐wave antenna arrays can be integrated with active circuits. The chapter includes 180 references to research papers describing advanced techniques developed in both industry and academia.

show abstract

Is the EDA Industry Ready for Cloud Computing?

Cited by 9 publications

References 12 publications

Combining informetrics and trend analysis to understand past and current directions in electronic design automation

Combining informetrics and trend analysis to understand past and current directions in electronic design automation

Characterizing and Optimizing EDA Flows for the Cloud

Radio‐Frequency‐Integrated Circuits

Contact Info

Product

Resources

About