Machine Learning for VLSI CAD: A Case Study in On-Chip Power Grid Design

2021 ◽  
Author(s):  
Sukanta Dey ◽  
Sukumar Nandi ◽  
Gaurav Trivedi
Keyword(s):  
Machine Learning ◽  
Power Grid ◽  
Vlsi Cad ◽  
Grid Design ◽  
On Chip

scholarly journals Power supply network design: a case study driven approach

2007 ◽  
Vol 5 ◽  
pp. 279-284 ◽  
Author(s):  
M. Eireiner ◽  
S. Henzler ◽  
T. Missal ◽  
J. Berthold ◽  
D. Schmitt-Landsiedel
Keyword(s):  
Power Supply ◽  
Power Distribution ◽  
Power Grid ◽  
Design Parameters ◽  
Distribution Grid ◽  
Specific Design ◽  
Problem Reduction ◽  
Grid Optimization ◽  
On Chip

Abstract. A study, based on product related scenarios, on power supply integrity issues is conducted. The effectiveness of specific design parameters depends strongly on the expected loading of the power distribution grid. Therefore, the commonly used approach to only use an even current distribution can lead to non-optimal power grid designs. For power grid optimization, a problem reduction from quadratic to linear order is presented. Simulations in a System-on-Chip (SoC) environment show, that power supply integrity mainly depends on the placing of the cores within the SoC die.

Paper2Wire – A Case Study of User-Centred Development of Machine Learning Tools for UX Designers

i-com ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 19-32
Author(s):  
Daniel Buschek ◽  
Charlotte Anlauff ◽  
Florian Lachner
Keyword(s):  
Machine Learning ◽  
Development Process ◽  
User Study ◽  
Concept Development ◽  
Lessons Learned ◽  
Design Tool ◽  
Learning Tools ◽  
Interface Elements ◽  
Industry Partner

Abstract This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.

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