Overview
Syllabus
Intro
Can we Learn on the Edge? Al systems need to continually adapt to new data collected from the sensors Not only inference, but also run back-propagation on edge devices
#Activation is the Memory Bottleneck, not #Trainable Parameters
Related Work: Parameter-Efficient Transfer Learning
Address Optimization Difficulty of Quantized Graphs
QAS: Quantization-Aware Scaling
Sparse Layer/Tensor Update
Find Layers to Update by Contribution Analysis
Tiny Training Engine (TTE)
Tiny Training Engine Workflow
Deep Gradient Compression: Reduce Bandwidth
Post-training testing (high accuracy)
Real-life testing
Taught by
tinyML