Length: 1/2 Day (4 hours)
Become Cadence Certified
Course Description
In this course, we show a broad view of the trends in artificial intelligence, including how machine learning is deployed in EDA tools, especially Cadence AI platforms. Then we dig a little deeper into what the requirements and techniques are to implement machine learning algorithms in the real world. Finally, we identify what type of hardware requirements are best suited to run these machine learning algorithms.
Learning Objectives
After completing this course, you will be able to:
- Summarize artificial intelligence
- Examine the domains where machine learning can be deployed
- Summarize the next-generation ML-based EDA tools from Cadence
- Examine and select appropriate ML models
- Identify the hardware and software required for machine learning
Software Used in This Course
None
Software Release(s)
None
Modules in this Course
- Introduction to Artificial Intelligence
- Machine Learning Requirements
- Machine Learning Techniques
- Model Selection
- Training the Model
- Hardware Computing Requirements
- The Future of Work in the Era of Artificial Intelligence
Audience
- ASIC Designers
- System Architects
- PCB Designers
- Verification Engineers
Prerequisites
Before taking this course, you need to
Have knowledge of ASIC design flows and a basic understanding of EDA tools and methodologies
Related Courses
Cadence Cerebrus Intelligent Chip Explorer
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