TinyM2Net: A Flexible System Algorithm Co-designed Multimodal Learning Framework for Tiny Devices

Explore a cutting-edge framework for multimodal learning on tiny devices in this 23-minute conference talk from the tinyML Research Symposium 2022. Delve into TinyM^2Net, a flexible system algorithm co-designed for resource-constrained environments, presented by PhD student Hasib-Al-RASHID from the University of Maryland Baltimore. Learn about the motivations behind this innovative approach, its key contributions, and the implementation of depth separable layers and mixed precision quantization. Discover real-world applications through case studies in COVID detection, object detection, and battlefield object detection. Gain insights into the framework's performance on Raspberry Pi 4 and understand its potential impact on edge computing and IoT devices.

Introduction
Motivations
TinyM2Net
Contributions
Depth Separable Layer
Mixed Precision Quantization
Dynamics Content
Case Study
Covid Detection
Object Detection
Battlefield Object Detection
Raspberry Pi 4
Summary
Questions