Efficient Architectural Design and Hardware Acceleration

Overview

This course explores the intersection of innovative model design and advanced hardware solutions. It begins with an introduction to efficient model architectures, focusing on optimization techniques for various applications. Participants will learn to develop mobile-friendly networks, ensuring seamless deployment on resource-constrained devices. The course emphasizes practical skills in utilizing hardware acceleration tools and libraries, followed by strategies to integrate these techniques with efficient architectures. The project showcases real-world applications of efficient medical diagnostics powered by hardware-aware model optimization, culminating in a comprehensive understanding of the entire ecosystem.

Syllabus

Introduction to Efficient Model Architectures

Explore efficient AI model architectures, hardware optimization, profiling tools, and hands-on exercises for selecting hardware and debugging performance bottlenecks.

Mobile-Friendly Networks and Deployment

Learn to design, optimize, and deploy efficient neural networks for mobile and edge platforms, considering hardware constraints and using hands-on modern techniques for model efficiency.

Hardware Acceleration Tools and Libraries

Explore tools and libraries for hardware acceleration across GPU, mobile, and edge, optimizing inference with TensorRT, LiteRT, OpenVINO, and ONNX Runtime.

Combining Efficient Architectures with Hardware Acceleration

Explore how efficient AI architectures and hardware acceleration combine for production, with real-world cases and hands-on GPU and cross-platform optimization exercises.

UdaciMed: Efficient Medical Diagnostics with Hardware-Aware AI

In this project, you will develop a comprehensive optimization pipeline for a scalable GPU deployment scenario. You will brainstorm optimization strategies for alternative deployment targets