A Future of Intelligent, Ubiquitous Devices

Edge AI is no longer an emerging technology—it is the driving force behind the next generation of IoT. The ability to process AI on-device is unlocking new possibilities and innovations across multiple industries. As hardware continues to evolve and deployment becomes easier, the future of AI and IoT is all about smarter devices, better decision-making, and limitless innovation at the edge.

Advancing Vehicle Diagnostics & Safety with Edge AI

Edge AI enables real-time analytics and AI-powered automation at the source, making it ideal for applications where microseconds count. The use cases are widely varied, with one novel implementation focusing on applying edge AI to automotive applications to enhance safety and diagnostics through auditory analysis. Conventional accelerometer-based collision detection systems, for example, can miss certain types of accidents such as rollovers or side swipes. Meanwhile, typical diagnostic tools may not detect all automotive faults, especially those identifiable through auditory cues.

The Automotive Challenge: Accurately and Reliably Detecting Faults

GlobalSense, a San Diego-based technology startup pioneering this new approach to automotive diagnostics, needed a solution that could accurately and reliably detect faults. Co-founder and CEO Dr. Saeid Safavi explains, “These devices are all based on acceleration and deceleration as the only sensory mechanism, and they miss 30% of collisions today simply because some collisions do not follow the algorithm for the threshold they set, like car rollovers or side swipes.”

A Faster, More Streamlined Solution with Edge AI

To create a solution that could accurately and reliably detect faults, GlobalSense needed access to vast amounts of high-quality sound data and a platform that could use that data to train and update specific detection models. They also required real-time data processing capabilities to improve the accuracy of their models quickly.

Results

GlobalSense leveraged Edge Impulse’s platform for a streamlined, iterative approach to AI model development and deployment. Through this process, GlobalSense uploaded daily datasets received from auctioned vehicles to Edge Impulse’s platform to test their models, adjusting and refining them based on real-world data.

Edge Impulse’s easy-to-use interface and robust features allowed GlobalSense’s team to quickly identify and correct issues, significantly improving the efficiency of their model training process. By leveraging Edge Impulse’s powerful platform, GlobalSense has created a scalable, high-performance solution that fills a critical gap in the automotive industry and beyond.

“Edge Impulse has enabled us to potentially save several million dollars per year for one customer by helping us deploy the smallest possible models, reducing our overall BOM costs,” says Safavi. He adds, “The Edge Impulse toolchain has helped us shorten our time to market for some products by an order of magnitude.”

What Challenges Are Holding Back Edge AI?

While edge AI is transforming industries far beyond just automotive, widespread adoption still faces obstacles that must be addressed before the technology realizes its full potential.

• Model Complexity and Performance — Striking the right balance between model complexity, accuracy, and resource utilization is critical to pushing the limits of edge devices.
• Hardware Constraints — Edge devices must balance performance and power efficiency, as many run on battery or low-power processors.
• Resource Constraints — Organizations often face budget limitations that must be balanced against innovation. Additionally, talent scarcity and the skills gap remain significant challenges.

Data Privacy and Security Concerns

As intelligent systems move closer to the edge, they handle vast amounts of personal, operational, and proprietary information. Although edge AI offers inherent advantages in data protection by processing information locally, it also introduces new and complex security challenges that organizations must carefully navigate.

Ethical Considerations

Concerns about privacy, bias, and security must be addressed. Keeping data on-device makes monitoring and regulation more difficult. When it comes to bias, improperly trained AI models could lead to unfair or inaccurate decision-making.

Overcoming Obstacles to Edge AI Deployment

Simplifying and streamlining the path to edge AI deployment is possible by providing developers with the right tools. Some solutions to eliminating roadblocks for developers include:

• No-Code/Low-Code AI Model Building — Making model development seamless and accessible.
• Minimizing Hardware Costs — Enhancing the feasibility of deploying advanced AI in resource-constrained environments.
• Data Collection — Curating quality data is often expensive and not always available in large quantities. Synthetic data generation can help offset this obstacle, enabling a new and efficient way to work with LLM-generated images, audio, and voice to enhance edge AI models.
• Data Labeling — Automated AI labeling can significantly reduce the time and effort required for manual annotation.
• Optimizing Model Performance — Developers need access to tools that allow them to test and fine-tune on-device model performance, gaining immediate insights into real-world performance.

Democratizing Edge AI to Accelerate IoT Innovation

Edge Impulse’s technology empowers developers to bring more AI products to market and helps enterprise teams rapidly develop production-ready solutions in weeks instead of years. As a recent addition to the Qualcomm Technologies family, Edge Impulse now provides developers with even more power and options to accelerate the creation of high-performance IoT devices, along with support for computer vision, audio, and speech recognition.

Developers can join Edge Impulse’s premier developer community for free at edgeimpulse.com/signup. Don’t miss our developer workshop at the IoT World Congress on Wednesday, May 14th, from 11:00 AM – 12:30 PM at the Hacking Stage.