Explore efficient on-device deep learning in this 25-minute conference talk from tinyML Asia 2021. Delve into the challenges and opportunities of edge computing as Yunxin Liu, Guoqiang Professor at Tsinghua University's Institute for AI Industry Research, discusses the shift from centralized cloud intelligence to distributed edge intelligence. Discover recent research on customizing affordable AI models for diverse edge devices and maximizing on-device model inference performance through heterogeneous computing resources. Learn about hardware-friendly neural networks, kernel-level latency prediction, mobile CPU utilization, and optimization techniques. Gain insights into the future of AI-empowered edge devices and applications in this comprehensive overview of on-device deep learning advancements.
Efficient On-Device Deep Learning: Challenges and Opportunities
Overview
Syllabus
Introduction
Ideal computing
Data explosion
Four directions
Two directions
Hardware friendly neural networks
Kernel level latency prediction
Mobile CPU utilization
Optimization
Results
Taught by
tinyML
Related Courses
-
Introduction to On-Device AI
-
Getting Started with Machine Learning at the Edge on Arm
-
Hardware-Aware Model Optimization in Arm Ethos-U65 NPU
-
Lightweight Deep Learning on Edge Devices - Energy Efficient Approaches
-
Using AI to Design Energy-Efficient AI Accelerators for the Edge
-
Power Efficient Neural Network Based Hardware Architecture for Biomedical Applications
