Deep Dive: GPU-Accelerated CNN & Transformer Internals

Unlock the secrets of high-performance deep learning by dissecting CNN and Transformer architectures on GPUs, mastering memory optimization, and parallelization techniques.

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GPU Architecture and Memory Management for Deep Learning

Unit 1: GPU Architecture Fundamentals

Unit 2: Memory Allocation Strategies

Unit 3: Memory Optimization Techniques

Unit 4: Profiling and Bottleneck Analysis

Computational Graphs and Performance Bottleneck Analysis

Unit 1: Understanding Computational Graphs

Unit 2: Performance Bottleneck Identification

Unit 3: Graph Optimization Techniques

Parallelization Strategies for CNNs and Transformers

Unit 1: Introduction to Parallelization

Unit 2: Data Parallelism in Practice

Unit 3: Model Parallelism for Large Models

Transformer Acceleration Techniques and Hardware Specialization

Unit 1: Architectural Innovations and Hardware Acceleration

Unit 2: KV Caching and Attention Optimization

Unit 3: Hardware-Specific Optimizations and Performance Analysis