Fastware

Overview

Explore optimization techniques for modern computing, from silicon-oriented methods to algorithm adjustments, enhancing performance in speech recognition, self-driving cars, and AI.

Syllabus

• Introduction to Fastware
Overview of Course Objectives
Importance of Optimization in Modern Computing
• Silicon-Oriented Optimization Techniques
Understanding Processor Architectures
Exploring GPU vs. CPU for AI Workloads
Hardware Accelerators and ASICs
Energy-Efficient Designs
• Algorithmic Optimization
Big O Notation and Complexity Analysis
Parallel Computing and Multithreading
Memory Management and Caching
Data Structures for Speed
• Optimization in Speech Recognition
Neural Network Optimization for Speech
Real-time Processing Challenges and Solutions
Latency Reduction Techniques
• Self-Driving Cars and Real-Time Processing
Sensor Data Fusion Optimization
Path Planning and Decision-Making Algorithms
Safety-Critical Real-Time Systems
• AI-Specific Performance Enhancements
Model Pruning and Quantization
Transfer Learning for Efficiency
Hyperparameter Tuning and AutoML
• Case Studies and Practical Applications
Case Study: Optimizing a Speech Recognition System
Case Study: Improving a Self-Driving Car Algorithm
Hands-on Lab: Applying Fastware Techniques
• Advanced Topics
Quantum Computing and Potential Impacts on Optimization
Future Trends in AI Hardware and Software Optimization
• Project and Evaluation
Capstone Project: Optimize a Real-World AI Application
Evaluation Criteria and Peer Review Process
• Course Conclusion and Future Directions
Recap of Key Concepts
Exploring Career Paths in AI Optimization

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Subjects

Conference Talks