Empowering AI Innovations for Tomorrow: Workshop on Edge Computing with TinyML

The rapid advancements in artificial intelligence (AI) and the growing adoption of edge computing have unlocked opportunities to address critical global challenges through sustainable innovation. Tiny Machine Learning (TinyML), a cutting-edge field where edge computing meets IoT and AI, enables resource-constrained devices to process data locally and make real-time decisions without reliance on centralized systems. This workshop will explore how TinyML is revolutionizing AI by democratizing its applications in industries such as agriculture, healthcare, manufacturing, and environmental sustainability.

Participants will gain hands-on experience, learn from case studies, and discuss emerging trendsin TinyML. The workshop aligns with the theme "AI Innovations for a Better Tomorrow" by showcasing the transformative potential of TinyML in advancing pervasive and real-world AI applications.

Objectives

- Introduce participants to the fundamental concepts of TinyML and its role in edge computing.
- Provide practical experience with development kits and tools such as Edge Impulse for TinyML implementation.
- Demonstrate real-world applications and case studies showcasing TinyML's impact across industries.
- Facilitate discussions on ethical, scalable, and sustainable innovations in TinyML.
- Foster collaboration and networking within the TinyML and edge AI community.

Motivation

With the rising demand for low-latency, energy-efficient AI applications in resource-constrained environments, TinyML offers an ideal solution to address these needs. From reducing carbon footprints to optimizing industrial processes, TinyML represents a sustainable and cost-effective technology that democratizes AI for widespread real-world adoption.

Intended Audience

- Undergraduate and graduate students in AI, computer science, and engineering.
- Researchers and professionals exploring IoT, edge computing, and machine learning.
- Developers and practitioners interested in deploying AI at the edge.
- Enthusiasts passionate about sustainable technology and innovative applications of AI.

Topics to be Covered

1. Introduction to TinyML and Edge Computing
- Overview of TinyML concepts, challenges, and opportunities.
- Key features: low power consumption, cost efficiency, and real-time processing.
2. Hands-On Experience with TinyML Development Kits
- Practical implementation using tools like Edge Impulse.
- Deploying AI models on microcontroller-based devices for real-world applications.
3. Real-World Applications of TinyML
- Sustainable Environmental Control in Smart Poultry Farming.
- Carbon Footprint Estimation.
- Predictive Maintenance in Industry 4.0.
- Estimating Shelf Life of Date Palm Fruits.
- Real-Time Anomaly Detection in Oil & Gas Operations.
4. Emerging Trends, Challenges, and Ethical Considerations in TinyML
- Scalability, sustainability, and privacy concerns.
- Advancements in hardware and software for TinyML.

Session Duration

Two sessions of 3 Hours: This duration will provide sufficient time for comprehensive coverage of topics, hands-on activities, and interactive discussions.

Relevance and Importance

The topics covered in this workshop address the urgent need for sustainable, efficient, and real-time AI solutions across industries, aligning perfectly with the conference theme. TinyML offers a transformative approach to making AI accessible and impactful in resource-constrained environments.

Content Outline

Welcome and Introduction to TinyML and Edge Computing
Hands-On Experience with TinyML Development Kits
Case Studies and Real-World Applications
Challenges, Trends, and Ethical Considerations
Q&A and Networking