2018 International Conference On Computer Aided Design

The Premier Conference Devoted to Technical Innovations in Electronic Design Automation

November 5-8, 2018Hilton San Diego Resort & Spa San Diego, CA

ACM SIGDA CADathlon 2018 at ICCAD

Tue, 2018-08-14 20:38 -- root
In the spirit of the long-running ACM programming contest, the CADathlon is a challenging, all-day, programming competition focusing on practical problems at the forefront of Computer-Aided Design, and Electronic Design Automation in particular. The contest emphasizes the knowledge of algorithmic techniques for CAD applications, problem-solving and programming skills, as well as teamwork. 

ACM Student Research Competition Poster Session

Tue, 2018-08-14 19:34 -- root
Sponsored by Microsoft Research, the ACM Student Research Competition (SRC) is an internationally recognized venue enabling undergraduate and graduate students who are members of ACM and ACM SIGDA to: 
• Experience the research world—for many undergraduates this is a first! 
• Share research results and exchange ideas with other students, judges, and conference attendees. 
• Rub shoulders with academic and industry luminaries. 
• Understand the practical applications of their research. 

Bias Buster Workshop @ ICCAD

Tue, 2018-08-14 19:34 -- root
Unconscious (or implicit) bias is believed to be a significant factor that inhibits inclusivity and acts to stall or even thwart important efforts for increasing and celebrating diversity. Everyone has implicit or unconscious biases, and because they are unconscious, we are unaware of them. In this workshop, the first of its kind at ICCAD, the basis of implicit bias is presented and effective tools for increasing awareness and mitigation of its unwanted effects are provided.

11th IEEE/ACM Workshop on Variability Modeling and Characterization

Tue, 2018-08-14 03:51 -- root

This workshop provides a forum to discuss current practices as well as near-future research aimed at addressing the ever-growing problems of variability/reliability and their impact on design performance and cost. The scope of the workshop further covers the detailed technical aspects of variability/reliability characterization, including compact modeling, stochastic simulation, test structure design, statistical CAD, and resilient design. The workshop also provides an opportunity to discuss modeling and characterization needs for emerging device technologies.

Machine Learning and Systems for Building the Next Generation of EDA Tools

Tue, 2018-08-14 03:51 -- root

This workshop covers the basics of machine learning, systems and infrastructure considerations for performing machine learning at scale, specialized hardware architectures for neural networks, and approaches for using machine learning for building the next generation of EDA tools.

International Workshop on Design Automation for Analog and Mixed-Signal Circuit

Tue, 2018-08-14 03:51 -- root

A substantial portion of modern integrated circuits are analog and mixed-signal (AMS) circuits that provide critical functionality such as signal conversion. Over the past several decades, aggressive scaling of IC technologies, and the integration of heterogeneous physical domains on a chip, substantially complicates the design of AMS circuits. On the one hand, their modeling and design becomes extremely complex. On the other hand, their interplay with the rest of the system-on-chip challenges design, verification and test.

First Workshop on Open-Source EDA Technology (WOSET)

Tue, 2018-08-14 03:51 -- root
Background: The cost and difficulty of IC design in advanced nodes have stifled hardware design innovation and raised unprecedented barriers to bringing new design ideas to the marketplace. Notably, commercial EDA tools have become both expensive and highly complex, as they are aimed at leading-edge, expert users. Unlike the thriving software community, which enjoys a large number of open-source operating systems, compilers, libraries and applications, the hardware community lacks such an ecosystem.

Hardware and Algorithms for Learning On-a-Chip (HALO)

Tue, 2018-08-14 03:51 -- root
In recent years, machine/deep learning algorithms has unprecedentedly improved the accuracies in practical recognition and classification tasks, some even surpassing human-level accuracy. However, to achieve incremental accuracy improvement, state-of-the-art deep neural network (DNN) algorithms tend to present very deep and large models, which poses significant challenges for hardware implementations in terms of computation, memory, and communication.


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