Watch Ensuring AI/ML Safety in Embedded Systems

AI in Embedded Systems: Safe, Smart, and Ready for the Real World

AI and ML are revolutionizing safety-critical industries like automotive, healthcare, and aerospace. These technologies offer remarkable autonomy and efficiency, but integrating them into resource-limited embedded systems where predictability, precision and safety are critical can be a significant challenge.

To mitigate these challenges, it’s critical to implement strategies like model pruning, model freezing, lots of testing, and rigorous adherence to functional safety standards like ISO 26262, IEC 62304 and other supporting safety standards.

Key Takeaways

• AI adoption in embedded systems is happening now, not sometime in the distant future.
• Safety, predictability, and compliance are the big hurdles developers face.
• Specialized hardware like NPUs and edge TPUs are changing what’s possible in embedded systems.
• Techniques like model pruning, quantization, and freezing make it possible to integrate AI into embedded systems.
• Ensuring safety when using AI requires guardrails, traceability, and lots of testing.

Why AI Is Transforming Embedded Systems

AI is making everything smarter. Ten years ago, your watch counted steps. Now it tracks your heart rate, checks for arrhythmias, and knows if you’re running, swimming, or snoozing. Companies are putting AI into medical devices, cars, energy systems—you name it. Even smarter self-driving taxis are showing up in some cities.

But, the AI inside a fitness tracker is nothing like the monster models that power tools like ChatGPT. Embedded AI means squeezing intelligence into gadgets with strict limits on space, power, and compute power. This isn’t just a trend—by 2030, it’s expected that 70% of embedded systems will be AI-enabled. The challenge: getting all the benefits of AI without blowing the budget (or blowing something up).

Big Problems: Safety, Security, Sizing, and Hardware

Safety and Compliance: The Must-Haves

In safety-critical settings, a slight delay or unpredictability can be catastrophic. A car’s AI-driven brake assistant can’t take a coffee break, and if a millisecond is lost, it could cost lives. Unfortunately, AI models are non-deterministic—they commonly give different answers for the same input, which doesn’t fly in regulated industries like automotive or aerospace.

Security is another problem. Messing with inputs to confuse an AI—like slapping a sticker on a stop sign—can fool a model, with real-world dangers.

Hardware Matters

Some chips are better than others at running AI on the edge. On-device NPUs and edge TPUs are leading the way for embedded AI—think faster, cheaper, and less power-hungry.

Making Models Fit: Pruning and Quantization

Big neural networks are nice, but try fitting one into a tiny device without running out of memory or draining the battery. Two ways to keep things tight:

1. Pruning: After training, remove neurons and connections that barely do anything. Pruned models are smaller, faster, and use less energy. Example: NASA’s Mars Rover used a pruned model to make its limited hardware run 30% quicker.
2. Quantization: Convert the model from 32-bit floating point to 16-bit, 8-bit or even 4-bit to shrink the size, reduce the computational load, memory usage, and power consumption.

Model Freezing and Predictability

Once an AI model is trained, tested, and passes all the checks, it gets “frozen.”

• No more last-minute tweaks.
• Every car, plane, or medical device will behave the same way in the same situation.
• Regulators like it because they know what’s going to happen next.

Big companies like Tesla lock their models so cars don’t just invent new moves on the fly and get into trouble.

Risk, Standards, and Guardrails

Making an AI system safe isn’t about removing all risk—it’s about managing it. Think:

• Formal verification: Prove that your AI won’t go rogue.
• Simulations: Run millions of scenarios before anything hits the road (ideally, literally).
• Redundancy: Have clones or backup AIs watching each other’s backs.
• Guardrails: Hard-coded rules that keep AI within set boundaries (like not letting a car blow through a speed limit, no matter how confident the model is that it’s fine).

You have to prove your safety case with documentation—with standards like ISO 26262 for automotive, IEC 62304 for medical, or new guidelines like ISO 8800 (for automotive AI).

Testing, Traceability, and Explainability

You still need to thoroughly test the system:

• Static and dynamic analysis catch functional defects throughout the SDLC.
• Code coverage: Make sure your test cases have tested every line of code.
• Traceability: Link every requirement, document every change, collect every test result, so you can show regulators an audit trail.
• Explainable AI (XAI): Tools like SHAP or LIME help debug and explain why the AI made a call, unearthing its black-box nature.

What’s Next for Embedded AI?

AI’s future in embedded systems is moving fast:

• On-device learning: No cloud, no delays—smarter, local, and private.
• Hardware keeps shrinking: Nanometer-scale chips mean more brains in less space.
• Better privacy and security: With techniques like federated learning and AI tools to counter next-gen cyber tricks.
• AI helping engineers build better AI: No more staring at code bugs for hours—AI can propose fixes.

Wrap-Up

AI is changing what embedded systems can do, but it’s bringing a truckload of safety and compliance questions. Luckily, pruning, quantization, specialized hardware, explainable AI, and strict testing/regulatory standards are making safe, certifiable systems that make the use of AI a reality in the embedded world. The future looks busy—and smart.