Machine learning models, particularly those deployed on edge devices, often face challenges when transitioning from development to real-world environments. Despite rigorous training, models may struggle with unexpected conditions, insufficiently represented scenarios, or limitations in data diversity. Expanding a dataset through additional data collection can be costly and time-consuming. This is where synthetic data comes in.
What is Synthetic Data?
Synthetic data is artificially generated data that mimics real-world examples, helping your model learn from more diverse scenarios — without you having to manually collect every possible case. Classic data augmentation techniques — like flipping, rotating, or color-shifting images — are simple ways of creating "new" data from what you already have. But synthetic data goes a step further.
- 3D-rendered images, useful for object detection and classification tasks
- Simulated sensor data, replicating real-world conditions for time-series analysis
By incorporating synthetic data, engineers can improve model robustness, reduce biases, and enhance model accuracy — critical factors for Edge AI applications where the unreliability of the real-world presents significant challenges.
When Should You Use Synthetic Data?
Synthetic data is particularly effective in scenarios where real-world data is scarce, difficult to obtain, or privacy-sensitive. Key use cases include:
- Rare event detection (e.g., detect rare defects and anomalies in manufacturing without affecting real-time production lines)
- Edge cases (e.g., simulating unusual lighting conditions, camera occlusions, and other environmental factors or weather conditions)
- Data privacy concerns (e.g., replacing sensitive real-world data)
- Small datasets (e.g., synthetic samples can supplement training datasets to improve model accuracy)
How to Integrate Synthetic Data into Your Edge AI Dataset
Here’s a short and sweet, step-by-step breakdown to integrating synthetically generated data into your edge AI training and/or testing datasets:
- Identify Gaps – Use tools like the Edge Impulse dataset explorer to find underrepresented classes or conditions.
- Choose a Synthetic Data Strategy – Simple augmentation vs. fully synthetic samples (e.g., rendering 3D objects for object detection).
- Generate & Validate – Create synthetic samples and evaluate how they impact model performance.
- Blend with Real Data – Don’t replace real-world data entirely — combine synthetic and real-world samples for balance.
- Train & Test – Run experiments in Edge Impulse, compare results, and iterate.
Impulse Experiments tool in Edge Impulse
Does Synthetic Data Actually Work?
Yes, synthetic data can dramatically improve model performance — but only if done correctly. A common practice is maintaining a 70/30 ratio between real and synthetic data, though this varies based on model requirements and your specific use case.
A few things to watch out for:
- Overfitting – If synthetic data isn’t varied enough, your model might memorize patterns instead of learning real-world features.
- Unrealistic Data Distribution – If your synthetic samples look nothing like real-world data, your model may struggle when deployed in actual environments.
- Maintaining an Appropriate Balance – Relying too heavily on synthetic data can introduce biases. Always keep real-world validation in the loop.
Some real-world examples where synthetic data is particularly useful:
- Autonomous vehicles use simulation-based training before testing in real environments
- Manufacturing defect detection systems are improved using artificially generated "defect" production line images
Edge Impulse provides many tools to help you integrate synthetic data into your training and testing datasets for your Edge AI projects:
- Impulse Experiments, for designing, training and testing multiple model architectures over the same datasets.
- NVIDIA Omniverse Integration, for quickly collecting computer vision synthetic datasets and automatic ingestion into your Edge Impulse project.
- Also, check out this blog post for Tips & Tricks on Creating a Robust Synthetic Dataset Using NVIDIA Omniverse from Hannah Moshtaghi!
- Synthetic data generation tab, a user-friendly interface to generate synthetic data for your projects, utilizing tools like DALL-E, Whisper, and Eleven Labs inside Edge Impulse.
- For more information, check out the announcement blog "Introducing Synthetic Data Generation in Edge Impulse"!
- Data Explorer, a visual tool to explore your dataset, find outliers or mislabeled data, and to help label unlabeled data.
Takeaways & Next Steps
Incorporating synthetic data into edge AI datasets offers a powerful means of improving model performance, addressing dataset limitations, and enhancing accuracy. By strategically blending synthetic and real-world samples, engineers can build more resilient models without the need for extensive data collection efforts.
Some key takeaways:
- Synthetic data is a powerful way to boost your dataset without costly data collection
- It’s especially useful for rare events, edge cases, and privacy-sensitive applications
- The key is balance — combine synthetic and real-world data for best results
Ready to experiment? Try adding synthetic data to your next Edge Impulse project and see how it impacts your model. Got an interesting use case? We’d love to hear about it! Show us your project at the Edge Impulse forum.