Small Devices, Big Headaches: A Story About Edge AI

Deploying AI on embedded systems isn’t just a technical challenge—it’s a test of ingenuity. From squeezing deep learning models onto tiny devices to navigating strict privacy laws, this is where innovation meets reality.

AI is everywhere—from autonomous vehicles to smart cities—but deploying it on embedded systems has been one of the toughest and most rewarding challenges of my career. Unlike cloud-based systems with virtually unlimited compute power, embedded systems demand precision, optimization, and compromise at every step.

Having worked on AI deployments at Tier Mobility and as the founder of ENVAIO, I’ve seen how difficult it can be to bring AI models from research labs to resource-constrained environments. Here, I’ll share the key hurdles we faced and the lessons learned along the way.

Why Does Edge AI Matter?

Edge AI enables computations directly on devices without relying on cloud connectivity. This reduces latency, improves data privacy, and can cut costs in the long term.

• At Tier Mobility, this meant deploying AI on scooters equipped with edge devices like Nvidia Jetson, Raspberry Pi or Qualcomm SoCs.
• At ENVAIO, it meant creating GDPR-compliant IoT devices that could process visual data locally while staying affordable for mobility and retail applications.

But it’s not all smooth sailing. Edge AI brings a host of trade-offs:

• How do you fit complex AI models onto hardware designed for minimal power consumption?
• How do you ensure reliability in chaotic environments like city streets or retail spaces?

The Challenges of Deploying AI on Embedded Systems

1. Hardware Limitations

Edge devices like Nvidia Jetson or Raspberry Pi are marvels of engineering, but their limitations can feel like roadblocks. These devices often lack the processing power or memory needed to run state-of-the-art AI models.

• At Tier, deploying convolutional neural networks (CNNs) for rider behavior analysis was a major challenge. Our initial models were too large and too slow.
• What worked: Techniques like model quantization and pruning reduced model size significantly without losing too much accuracy.

Lesson learned: Every optimization involved trade-offs in accuracy. Finding the right balance took trial and error.

2. Real-Time Data Processing

City streets are unpredictable environments, and our rider assistance systems at Tier had to process real-time data from multiple sensors, including cameras and accelerometers.

• Early iterations tried to include too many features, overloading the system.
• By focusing on core functionalities—like detecting tandem riding behaviors—we simplified workflows while maintaining value.

Pro tip: Prioritize simplicity. Less is often more when it comes to edge AI.

3. Deployment and Maintenance

Unlike cloud systems, embedded devices can’t be easily updated or debugged remotely.

• At ENVAIO, IoT devices needed to operate reliably for months without maintenance.
• Errors in deployed software meant expensive physical recalls, so we leaned heavily on CI/CD pipelines and automated testing to catch issues early.

Result: Reduced post-deployment failures and greater confidence in our systems.

4. Data Privacy and Compliance

For ENVAIO, GDPR compliance was non-negotiable.

• Processing data locally avoided transmitting sensitive information to the cloud, aligning with privacy laws.
• However, this put additional strain on already limited processing power.

What We Learned Along the Way

Every success came with its own lessons:

1. Collaboration is key.
   At Tier, cross-functional teams aligned on priorities to ensure technical trade-offs matched business goals.
   Example: When stakeholders requested new features, we worked together to explain how they’d impact latency and cost.

2. Iterate relentlessly.
   Early deployments often failed. For instance, initial models misclassified normal riding as tandem riding too often.
   Collecting more data and refining models over several iterations improved reliability.

3. Use the right tools.
   Tools like TensorRT and ONNX Runtime helped optimize models, cutting inference times by over 50%.

4. Start simple.
   At ENVAIO, trying to pack every feature into our first IoT devices overwhelmed both the hardware and the team.
   Scaling back to core functionalities improved stability and allowed us to focus on refining our product-market fit.

The Business Perspective

Edge AI isn’t just a technical challenge—it’s a strategic decision. While the upfront investment in hardware and engineering can be significant, the long-term benefits are transformative.

• At Tier, rider assistance and predictive maintenance systems improved user experience and reduced operational costs.
• At ENVAIO, privacy-first devices opened doors to markets with stringent compliance demands.

If your business is considering edge AI, ask yourself:

• Does the application demand low latency or real-time processing?
• Are privacy or connectivity concerns critical?
• Can the initial costs be justified by the expected ROI?

The Road Ahead

Edge AI is advancing rapidly. New hardware and techniques like federated learning are pushing the boundaries of what’s possible.

For me, these developments are exciting but humbling. Every project reminds me of how much there is still to learn—about the technology, about collaboration, and about transforming ideas into reality. Deploying AI on embedded systems is rarely straightforward, but it’s worth the effort.

After all, the closer we bring AI to the edge, the closer we bring innovation to the people who need it most.

What are your biggest challenges or successes in deploying AI systems? I’d love to hear your thoughts—let’s learn from each other!