MONDAY February 25, 1:45pm - 3:15pm | Fir
Deep learning continues to be a very hot topic. Deep learning first came to public attention because of its success in the field of object recognition in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), and now delivers state-of-the-art solutions in many diverse problem domains. New applications and new success stories seem to emerge daily, from medical imaging to autonomous vehicles. Deep learning is already impacting many people working in the design and verification of electronic systems, and will soon impact many more. Are you ready?
Deep learning impacts design and verification engineers in two ways: the appearance of deep learning algorithms within the products we design and verify, and the use of deep learning within design and verification tools. An increasing number of engineers working in semiconductor and systems businesses now have to support customer designs that incorporate neural network processors. While deep learning algorithms are starting to appear in Electronic Design Automation (EDA) tools, there is still confusion over exactly what distinguishes deep learning from other approaches to machine learning, and many of the questions about the applicability of deep learning to hardware verification still remain to be answered.
This workshop gives a basic introduction to the subject of deep learning specifically aimed at the interests of design and verification engineers. This workshop will help you to understand exactly what deep learning is, how deep learning differs from other approaches to machine learning, and how to go about choosing the right approach for your particular problem. This workshop will help you to cut through some of the fog that sometimes surrounds discussions of deep learning by taking a no-nonsense look at the technical heart of the matter: what deep learning really is, what it is good for, and what it is not good for. This workshop will give you an understanding of the elements that make up current neural network architectures, and what makes them successful.
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