Overcoming Challenges in Developing Machine Learning Models for Edge Devices
In an interview with Alessandro Grande, Head of Product at , the discussion focused on developing machine learning models for resource-constrained edge devices and how to overcome the challenges. Grande provided insightful perspectives on the current struggles, ways Edge Impulse is helping address these issues, and the tremendous potential of on-device AI.
Primary Pain Points
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- Difficulties determining optimal data collection strategies
- Scarcity of AI expertise
- Cross-disciplinary communication barriers between hardware, firmware, and data science teams
Optimizing for Edge Environments
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- Minimize required sensor data
- Select efficient neural network architectures
- Use compression techniques like quantisation
- Balance sensor and hardware constraints against functionality, connectivity needs, and software requirements
Edge Impulse
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- Enables engineers to validate and verify models themselves pre-deployment using common ML evaluation metrics
- Seamlessly integrates with all major cloud and ML platforms
- Aims to address the challenges of developing machine learning models for edge devices
- Provides personalised health insights without reliance on the cloud, e.g., sleep tracking with.
- Offers opportunities for preventative industrial maintenance via anomaly detection on production lines.
- Has the potential to greatly enhance utility and usability in daily life, offering more useful technology and improved quality of life.
Unlocking the Potential of AI on Edge Devices
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Overcoming current obstacles inhibiting adoption is crucial to unleash the full possibilities of edge AI.
Grande and other leading experts provided deep insights at this year’s event on how to break down the barriers and unlock the potential of AI on edge devices.
“I’d love to see a world where the devices that we were dealing with were actually more useful to us,” concludes Grande.
