Key Hurdles with Edge AI Adoption
In an interview at AI & Big Data Expo, Alessandro Grande, Head of Product at Edge Impulse, discussed the challenges of developing machine learning models for resource-constrained edge devices. Grande highlighted three primary pain points companies face when attempting to productise edge machine learning models, including difficulties determining optimal data collection strategies, scarce AI expertise, and cross-disciplinary communication barriers between hardware, firmware, and data science teams.
Strategies for Lean and Efficient Models
When asked how to optimise for edge environments, Grande emphasized the importance of minimising required sensor data. He explained that companies often struggle with determining what data is enough, what data should be collected, and what data from which sensors should be collected. Grande also suggested selecting efficient neural network architectures and using compression techniques like quantisation to reduce precision without substantially impacting accuracy.
Transformative Potential of On-Device Intelligence
Grande highlighted innovative products already leveraging edge intelligence, such as sleep tracking with Oura Ring, which has sold over a billion pieces. He also mentioned exciting opportunities in preventative industrial maintenance via anomaly detection on production lines. Grande sees massive potential for on-device AI to greatly enhance utility and usability in daily life, providing actionable suggestions and responsive experiences not previously possible.
Conclusion
Unlocking the potential of AI on edge devices hinges on overcoming current obstacles inhibiting adoption. Grande and other leading experts provided deep insights at this year’s AI & Big Data Expo on how to break down the barriers and unleash the full possibilities of edge AI. With the right strategies and approaches, edge AI can greatly enhance utility and usability in daily life.
FAQs
Q: What are the primary pain points companies face when attempting to productise edge machine learning models?
A: Companies face difficulties determining optimal data collection strategies, scarce AI expertise, and cross-disciplinary communication barriers between hardware, firmware, and data science teams.
Q: How can companies optimise for edge environments?
A: Companies can optimise for edge environments by minimising required sensor data, selecting efficient neural network architectures, and using compression techniques like quantisation to reduce precision without substantially impacting accuracy.
Q: What are some innovative products already leveraging edge intelligence?
A: Some innovative products already leveraging edge intelligence include sleep tracking with Oura Ring and preventative industrial maintenance via anomaly detection on production lines.
Q: What is the transformative potential of on-device intelligence?
A: On-device intelligence has the potential to greatly enhance utility and usability in daily life, providing actionable suggestions and responsive experiences not previously possible.
