System Software for Machine Learning at the Edge - tinyML Asia 2020

Explore system software approaches for machine learning at the edge in this tinyML Asia 2020 conference talk. Delve into the challenges and opportunities of deploying ML on mobile and IoT devices, focusing on compiler and runtime software strategies to maximize hardware potential. Learn about deep neural network optimizations, workload partitioning, and voltage-frequency scaling techniques for orchestrating on-chip compute resources. Discover how these methods can achieve low-power, real-time edge ML performance across various applications, from gesture recognition to fitness tracking. Gain insights into the evolving landscape of heterogeneous system-on-chips, TensorFlow Lite architecture, and the role of compilers in addressing fundamental problems in edge computing.

Introduction
Presentation
Machine Learning at the Edge
Finger and Hand Gesture Recognition
TI Sensor Tag
Arm Cortex M3
Fitness Tracking
Wearable SOC Trends
heterogeneous SOCs
Tensorflow Lite
Architecture
Accelerators
Universal Accelerators
Software Defined Hardware
GovTech IoT Stack
Scalable Compilation
Application Scenario
Questions
Singapore Smart City Ranking
Smart Nation Initiative
Heterogeneity
Fundamental Problems
Compilers
Role of Compilers
Thank you
Sponsor
ARM
Cortex
Media Partners
Conclusion