Understanding computer architecture makes you a better programmer, a better AI engineer, and a better systems designer. This course covers CPU design, memory hierarchy, caches, pipelines, and GPU architecture — the hardware foundation that every software system runs on.
This is a text-first course that links out to the best supporting material on the internet instead of trying to replace it. The goal is to make this the best course on computer architecture you can find — even without producing a single minute of custom video.
Day 5 focuses on GPU architecture — why GPUs are used for AI training, how CUDA works, and what VRAM limitations mean for model size.
Every architecture concept is explained with a diagram before the technical details. The mental model comes first.
Computer Organization and Design by Patterson and Hennessy is the canonical textbook. This course links to relevant sections.
Each day is designed to finish in about an hour of focused reading plus worked examples. No live classes, no quizzes.
Each day stands alone. Read them in order for the full picture, or jump straight to the day that answers the question you have today.
The fetch-decode-execute cycle, ALU, registers, control unit. How a CPU executes instructions. The architectural decisions that determine performance.
Registers, L1/L2/L3 cache, RAM, storage. Latency numbers every programmer should know. Why memory hierarchy design determines real-world program performance.
Cache hit/miss, direct-mapped vs set-associative, replacement policies, cache coherence. Writing cache-friendly code that runs 10x faster.
Pipeline stages, hazards (data, control, structural), branch prediction, out-of-order execution. Why modern CPUs are so fast despite clock speed limits.
SIMD parallelism, GPU vs CPU design philosophy, CUDA cores, VRAM, tensor cores. Why GPUs are used for AI training and what hardware limits model size.
Instead of shooting our own videos, we link to the best deep-dives already on YouTube. Watch them alongside the course. All external, all free, all from builders who ship this stuff.
Visual explanations of CPU architecture — the fetch-decode-execute cycle, ALU, and how instructions run.
How memory hierarchy works — L1/L2/L3 cache, RAM, and the latency numbers that explain modern program performance.
How instruction pipelining works, the types of hazards, and how modern CPUs achieve high throughput through parallelism.
How GPUs differ from CPUs architecturally, why they're used for AI training, and what VRAM limits mean for model size.
How CPUs predict branches to avoid pipeline stalls — and the Spectre/Meltdown implications of speculative execution.
Walkthrough content based on Patterson and Hennessy's Computer Organization and Design — the canonical textbook.
The best way to go deeper on any topic is to read canonical open-source implementations. These repositories implement the core patterns covered in this course.
Curated computer science resources including computer architecture, operating systems, and hardware references.
NVIDIA's official CUDA sample code — the canonical reference for GPU programming and the architecture Day 5 covers.
Curated list of hardware design tools, simulators, and learning resources for computer architecture study.
The RISC-V instruction set architecture manual. The cleanest ISA design for understanding computer architecture principles.
You took architecture in school but it felt abstract. This course connects the concepts to the hardware that runs your code every day.
You train models and want to understand why GPU architecture matters, what VRAM limits mean, and how to write faster GPU code.
You optimize software and want to understand the hardware constraints you're optimizing against — cache hierarchy, pipeline hazards, memory bandwidth.
The 2-day in-person Precision AI Academy bootcamp covers hardware, systems programming, and AI infrastructure — hands-on with Bo. 5 U.S. cities. $1,490. 40 seats max. June–October 2026 (Thu–Fri).
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