How to Quickly Implement Efficient AI and ML at the Edge
Designers looking to implement artificial intelligence (AI) and machine learning (ML) at the Edge, particularly for voice user interfaces (VUIs) and sound event detection (SED), need to find a way to balance cost, performance, and power, particularly for battery-powered devices. While executing AI/ML at the edge ensure privacy and low latency, two key contributors to a positive user experience, a poorly implemented, expensive, and unreliable design and can greatly impact that experience.
Attendees will come away with an understanding of:
- Why Edge AI/ML is so critical and important to get right
- What challenges designers face when implementing AI/ML
- General architectural approaches toward addressing these challenges
- Advances in algorithm compression techniques toward improved efficiency and performance
- What to look for in a toolchain to ensure broad compatibility across popular AI frameworks, while allowing optimization and accelerated, cost-effective deployment
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