Deep Learning Performance Architect Learning Track
Master GPU architecture, AI workload analysis, and performance optimization for next-generation deep learning accelerators
Prerequisites
- MS/PhD in Computer Science, Electrical Engineering, or equivalent experience
- Strong computer architecture fundamentals (CPU, GPU, memory hierarchies)
- Proficiency in C++ and Python programming
- Understanding of parallel computing concepts
- Basic knowledge of deep learning and neural networks
- Experience with performance analysis tools
Learning Outcomes
- Design and evaluate next-generation AI accelerator architectures
- Benchmark and analyze deep learning workloads across single and multi-node systems
- Develop high-level simulators and analysis tools for AI hardware
- Perform comprehensive PPA (Performance, Power, Area) analysis for hardware features
- Optimize GPU architectures for training and inference workloads
- Understand and optimize transformer-based model architectures at hardware level
- Design efficient interconnect fabrics for multi-node AI training
- Communicate complex technical concepts to cross-functional teams
- Evaluate system-level architectural trade-offs for AI workloads
- Stay current with emerging trends in deep learning hardware
Track Modules
System & Microarchitecture Deep Dive
End-to-end reasoning about compute + data pathologies with evidence-based fixes for CPU pipelines, GPU occupancy, and memory hierarchies
Tools & Methods: Top-Down, CDRD, and Roofline
Turn counters and simple models into clear diagnoses and action items using systematic performance analysis methodologies
Modeling & Simulation
Strategic simulation methodology: choose the right simulation paradigm and fidelity level; ask targeted questions, validate against reality
Power & Thermal Awareness — From Activity to perf/W
Translate simulated activity into power/thermal behavior and communicate perf/W trade-offs credibly using McPAT and HotSpot
Validation & Measurement — Trust, But Verify
Cross-validate models with real counters, quantify uncertainty, and communicate limits in performance analysis
Deep Learning ASIC Architecture
Master the design principles of custom AI accelerators, from tensor processing units to emerging neuromorphic architectures
AI Workload Analysis & Benchmarking
Master the techniques for profiling, characterizing, and optimizing deep learning workloads across different hardware platforms
Advanced GPU Architecture for ML
Deep dive into modern GPU architectures optimized for machine learning, from latest datacenter GPUs to next-generation designs
Transformer Hardware Optimization
Deep dive into optimizing hardware architectures for transformer-based models, from attention mechanisms to large language model inference
Interconnect Fabrics for AI Systems
Design and optimization of high-performance interconnects for distributed AI training and inference systems
PPA Analysis Methodologies
Master Performance, Power, and Area analysis techniques for evaluating hardware design trade-offs in AI accelerators
Multi-Node AI Training Systems
Master the design and optimization of distributed AI training systems across hundreds of nodes and GPUs
AI Hardware Simulation & Modeling
Develop high-fidelity simulators and performance models for evaluating next-generation AI accelerator architectures
Deep Learning Performance Architect Learning Track
Overview
This comprehensive learning track prepares you for senior-level roles in AI hardware architecture, specifically targeting Senior Deep Learning Performance Architect positions at leading technology companies. You'll master the intersection of computer architecture, parallel computing, and deep learning optimization.
What Makes This Track Unique
This track uniquely combines theoretical computer architecture with practical AI workload optimization. Unlike general architecture courses, you'll work directly with transformer models, GPU programming, and real AI accelerator design challenges that reflect current industry needs.
Learning Journey
Phase 1: Architecture Foundations (Weeks 1-3)
Build solid understanding of modern computer architecture principles, performance analysis methodologies, and hardware-software co-design concepts essential for AI accelerator development.
Phase 2: GPU & AI Accelerator Deep Dive (Weeks 4-6)
Master GPU microarchitecture, tensor processing units, and custom AI ASIC design. Learn how modern AI workloads stress different architectural components and optimization strategies.
Phase 3: Workload Analysis & Optimization (Weeks 7-9)
Develop expertise in benchmarking AI workloads, identifying performance bottlenecks, and optimizing hardware configurations for training and inference scenarios.
Phase 4: Advanced Systems & Simulation (Weeks 10-12)
Learn multi-node system design, interconnect optimization, and develop skills in building simulators and analysis tools for evaluating next-generation architectures.
Industry Relevance
This track is designed specifically for the evolving AI hardware landscape. Every module addresses real challenges faced by leading technology companies as they develop next-generation AI accelerators.
Key Industry Focus Areas:
- GPU Architecture: Advanced graphics processing optimization and CUDA ecosystem
- Tensor Processing Units: Custom AI accelerator design principles and optimization
- Framework Optimization: PyTorch and TensorFlow performance acceleration
- Edge AI Systems: Neural processing architectures for mobile and embedded devices
- Custom Silicon: AI-specific ASIC and accelerator development
Post-Completion Opportunities
Graduates of this track are prepared for roles at:
- GPU and Processor Companies: Leading graphics and compute processor manufacturers
- Cloud Providers: Major cloud platforms developing custom silicon for AI workloads
- AI Companies: Machine learning companies focused on infrastructure optimization
- Semiconductor Companies: Chip designers developing AI IP and specialized processors
- Automotive Technology: Companies developing autonomous driving AI hardware systems
This track represents the cutting edge of AI hardware architecture education, preparing you for the most challenging and rewarding roles in the intersection of computer architecture and artificial intelligence.