Get in Touch
 Duration 21 hours

Course Outline

Introduction to AI in Semiconductor Design Automation

  • Overview of AI applications within EDA software
  • Challenges and prospects in AI-driven design automation
  • Case studies highlighting successful AI integration in semiconductor design

Machine Learning for Design Optimization

  • Introduction to machine learning methods for design refinement
  • Feature selection and model training for EDA software
  • Practical uses in design rule checking and layout optimization

Neural Networks in Chip Verification

  • Understanding neural networks and their function in chip validation
  • Deploying neural networks for error identification and correction
  • Case studies on neural network implementation in EDA software

Advanced AI Methods for Power and Performance Optimization

  • Examining AI techniques for power and performance evaluation
  • Integrating AI models to maximize power efficiency
  • Real-world instances of AI-driven performance improvements

Customizing EDA Tools with AI

  • Tailoring EDA software with AI for specific design hurdles
  • Building AI plugins and modules for existing EDA platforms
  • Practical exercises involving popular EDA tools and AI integration

Future Trends in AI for Semiconductor Design

  • Emerging AI technologies in semiconductor design automation
  • Future trajectories of AI-driven EDA software
  • Preparing for advancements in the AI and semiconductor sectors

Summary and Next Steps

Requirements

  • Background in semiconductor design and EDA software
  • In-depth understanding of AI and machine learning methodologies
  • Knowledge of neural network architectures

Target Audience

  • Semiconductor design engineers
  • AI specialists within the semiconductor sector
  • Developers of EDA tools

Number of participants


Price per participant

Open Training Courses require 5+ participants.

Testimonials (2)

Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.

Matthew Gay - Tarsus Pharmaceuticals
Course - Artificial Intelligence (AI) Overview

Working from first principles in a focused way, and moving to applying case studies within the same day

Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking

Upcoming Courses

Advanced AI Techniques for Semiconductor Design Automation

2026-12-07 09:30
21 hours
Riga, Old Town
4500 EUR (Online)
5100 EUR (Classroom)

Advanced AI Techniques for Semiconductor Design Automation

2026-12-21 09:30
21 hours
Riga, Old Town
4500 EUR (Online)
5100 EUR (Classroom)

Advanced AI Techniques for Semiconductor Design Automation

2027-01-04 09:30
21 hours
Riga, Old Town
4500 EUR (Online)
5100 EUR (Classroom)

Advanced AI Techniques for Semiconductor Design Automation

2027-01-18 09:30
21 hours
Riga, Old Town
4500 EUR (Online)
5100 EUR (Classroom)

Advanced AI Techniques for Semiconductor Design Automation

2027-02-01 09:30
21 hours
Riga, Old Town
4500 EUR (Online)
5100 EUR (Classroom)

Related Courses

Artificial Intelligence (AI) in Automotive

 14 Hours
Discover how Machine Learning and Deep Learning are reshaping the automotive landscape. This Latvia course examines key concepts spanning from simple automation to autonomous decision-making, covering neural networks and practical TensorFlow implementations for real-world use cases.
Read more...

Artificial Intelligence (AI) Overview

 7 Hours
This introductory course in Latvia delves into AI fundamentals, covering topics from intelligent agents to machine learning. It empowers executives and architects to evaluate emerging AI trends, incorporate practical solutions, and boost business agility through strategic automation.
Read more...

AlphaFold: AI-Driven Protein Structure Prediction and Interpretation

 7 Hours

This instructor-led, live training in Latvia (available online or onsite) is designed for biologists who want to understand the workings of AlphaFold and utilize its models to guide their experimental studies.

By the end of this session, participants will be equipped to:

  • Understand the core principles of AlphaFold.
  • Learn how the system operates.
  • Learn to interpret AlphaFold predictions and outcomes.
Read more...

Artificial Neural Networks, Machine Learning, Deep Thinking

 21 Hours
This three-day program in Latvia provides an in-depth look into the theory and application of Artificial Neural Networks, Machine Learning, and Deep Learning. Participants will investigate various network architectures, learning mechanisms, and underlying mathematical principles, progressing from fundamental perceptrons to sophisticated deep learning techniques.
Read more...

Applied AI from Scratch in Python

 28 Hours
This hands-on training in Latvia guides programmers in constructing AI models from first principles using Python. Participants will gain proficiency in supervised learning, neural networks, and unsupervised techniques via scikit-learn and Apache Spark. The course emphasizes practical Jupyter-based exercises to solve real-world problems.
Read more...

Creating Custom Chatbots with Google AutoML

 14 Hours

This live, instructor-led training, available online or on-site, is tailored for participants with diverse skill levels who intend to use Google's AutoML platform to develop customized chatbots for various applications.

Upon completion of this course, participants will be equipped to:

  • Grasp the core principles of chatbot creation.
  • Access and navigate the Google Cloud Platform and AutoML services.
  • Curate and prepare datasets for training chatbot models.
  • Build and assess custom chatbot models using AutoML.
  • Deploy and integrate chatbots across multiple platforms and communication channels.
  • Track and refine chatbot performance for sustained effectiveness.
Read more...

Pattern Recognition

 21 Hours

This instructor-led, live training in Latvia (delivered online or onsite) provides an introductory overview of pattern recognition and machine learning. It explores practical applications within statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

By the conclusion of this training, participants will be able to:

  • Apply core statistical methods to pattern recognition.
  • Employ key models, including neural networks and kernel methods, for data analysis.
  • Implement advanced techniques for complex problem-solving.
  • Boost prediction accuracy by integrating various models.
Read more...

Deep Reinforcement Learning with Python

 21 Hours
This instructor-led course in Latvia delves into both the theoretical foundations and practical execution of Deep Reinforcement Learning using Python. Participants will construct and train DRL agents using TensorFlow or PyTorch, leveraging essential algorithms like DQN and PPO to address complex real-world challenges.
Read more...

Edge AI with TensorFlow Lite

 14 Hours

This live, instructor-led training session, available in Latvia format (online or on-site), is designed for intermediate developers, data scientists, and AI professionals aiming to utilize TensorFlow Lite for Edge AI solutions.

By the conclusion of this program, participants will be capable of:

  • Comprehending the essentials of TensorFlow Lite and its function in Edge AI.
  • Creating and optimizing AI models with TensorFlow Lite.
  • Deploying TensorFlow Lite models onto various edge devices.
  • Leveraging tools and methods for model conversion and efficiency optimization.
  • Implementing real-world Edge AI applications using TensorFlow Lite.
Read more...

Kubeflow Essentials: Build, Train & Serve with Kubernetes

 14 Hours
This practical training in Latvia empowers you to develop, train, and deploy machine learning models on Kubernetes using Kubeflow. You will master navigating the ecosystem, creating scalable pipelines, and overseeing production-ready workloads through industry best practices.
Read more...

Kubeflow Fundamentals

 28 Hours

This live, instructor-led session in Latvia, offered online or onsite, is designed for developers and data scientists eager to build, deploy, and manage machine learning workflows on Kubernetes.

By the end of the program, attendees will be capable of:

  • Installing and setting up Kubeflow in both on-premise and cloud environments.
  • Constructing, deploying, and managing ML workflows that utilize Docker containers and Kubernetes.
  • Running full machine learning pipelines across diverse architectures and cloud platforms.
  • Using Kubeflow to launch and manage Jupyter notebooks.
  • Building ML training, hyperparameter tuning, and serving workloads on multiple platforms.
Read more...

Advanced Analytics with RapidMiner

 14 Hours

This instructor-led, live training in Latvia (online or on-site) is designed for intermediate data analysts looking to learn how to use RapidMiner for estimating and projecting values, as well as employing analytical tools for time series forecasting.

By the end of this program, participants will be able to:

  • Implement the CRISP-DM methodology, choose appropriate machine learning algorithms, and optimize model construction and performance.
  • Utilize RapidMiner to estimate and project values, and apply analytical tools for time series forecasting.
Read more...

Understanding Deep Neural Networks

 35 Hours
This 35-hour course in Latvia covers the fundamentals of deep neural networks, including CNNs, RNNs, and generative models such as GANs. Participants will gain hands-on experience with Theano and TensorFlow, learning to build, train, and deploy production-grade deep learning models for real-world applications.
Read more...

Explainability in Deep Learning: Demystifying Black-Box Models

 21 Hours

This instructor-led, live training in Latvia (delivered online or onsite) is designed for advanced professionals aiming to explore cutting-edge XAI techniques for deep learning, with a strong focus on building interpretable AI systems.

By the end of this program, participants will be able to:

  • Grasp the complexities of explainability within deep learning architectures.
  • Deploy advanced XAI methods for neural networks.
  • Interpret and decode the decisions made by deep learning models.
  • Manage the trade-offs between predictive accuracy and transparency.
Read more...

Related Categories

Machine Learning
Machine Learning