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This panel surveys the current state of TinyML, covering ultra-low-power neural-network architectures, hardware acceleration, energy constraints, embedded systems, and practical use cases. It also considers feasibility, tradeoffs, and challenges in deploying machine learning on constrained devices.
Syllabus
Introduction
What kind of AI do we need
Current neural network architectures
Hardware acceleration for neural networks
Power efficiency
Summary
Intel
Etienne
Commercial applications
Is TinyML a disruptive technology
How do we know if a use case is doable
Negative use cases
Positive use cases
Tradeoffs
Use cases
Question
Point of view
IoT
Voice Command
Face ID
System Dimensions
The Problem
Conclusion
Object detection
Embedded vs cloud
AI and Embedded Systems
Challenges
Risk 5 and AI modeling
TinyML vs microcontrollers
Distributed AI
Taught by
tinyML