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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 multi-choice knapsack mapping that reduces costs by 35.29%., Presented at DATE2021

Open-source EDA is rapidly enabling new waves of innovation on many fronts. For academic researchers, it speeds up the lifecycle of scientific progress and makes research results relevant to modern industry practice. For EDA professionals and the industry ecosystem, open-source EDA is a complement and booster to commercial EDA. For the IC design community, recent releases under permissive licenses make it possible for design engineers as well as hobbyists to take ideas to manufacturing-ready layout at essentially zero cost.This full-day tutorial will review the very latest progress in open-source EDA, focusing on the digital RTL-to-GDS flow. In particular, the tutorial will make a deep dive into The OpenROAD Project https://theopenroadproject.org/, which brings new open-source tools and machine learning into a “no human in the loop” RTL-to-GDS flow. OpenROAD’s key components will be reviewed, along with how the overall system is architected. Here, we will provide live / hands-on demos on using the OpenROAD flow and integrating it into existing design flows. We will then turn to industry-compatible open-source infrastructure, and then demonstrate how OpenROAD’s continuous integration (CI) system works in order to welcome new contributions and give a low-overhead start for design tool and methodology innovation. Importantly, open-source EDA and the goal of “self-driving”, no-humans IC design puts a spotlight on machine learning that reduces design effort and schedule. We will show examples of important machine learning use cases and proof points in the RTL-to-GDS flow, along with demos of an open-source, freely portable integrated metrics collection system.The target audience of this tutorial includes EDA researchers and developers, graduate students and professors, and IC design methodologists, physical design engineers, and managers. Whether you are a veteran in EDA / IC design or just starting a career, the tutorial will give a new perspective and a roadmap to use and contribute to open-source EDA.

This paper presents our experience in using a project-based learning approach in a senior-level class, which is not a traditional candidate for learning through projects in the CS curriculum. The objective of this class was to expose students to computer science applications of probabilistic analysis and stochastic processes. The class project expected the students to apply the concepts of Markov Chains constructed during in-class lectures to develop an understanding of the ranking algorithms used by the widely popular search giant, Google. These ranking algorithms sort web pages according to their relevance to a search query. Based on their understanding, the students were also expected to identify and document some strategies that can be employed by owners to improve the rank of their web sites. We found that our project, that guided the students through the four phases of project-based learning, led to unprecedented outcomes in the students' understanding of the subject. Not only did they apply the in-class knowledge to cultivate an understanding of a real-world problem and its solution, but they were also able to project strategies that were external to the class material. In the end-of-the-semester course evaluation, students commented that the project was very cool and well thought out, informally validating the value of our project-based approach to their learning.