Compute Is Cheap, Data Is Expensive - explore with chatgpt

 

**“Compute Is Cheap, Data Is Expensive:

A Practical Primer on GPUs, TPUs, and AI Hardware”**

Why this works

• Instantly communicates the core insight

• Memorable and quotable

• Signals systems-level thinking (engineers love this)

• Non-intimidating for newcomers

This title alone explains why the hardware exists.

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Strong Alternatives (Depending on Tone)

“Why AI Hardware Looks the Way It Does”

Subtitle: From Matrix Multiplication to GPUs and TPUs

✔ Excellent for broad audiences
✔ Very “TPU Deep Dive”-like

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**“From MatMul to Megawatts:

Understanding GPUs, TPUs, and Scaling AI”**

✔ Slightly more dramatic
✔ Good if you want to emphasize scaling and systems

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“The Shape of AI Hardware”

Subtitle: How Workloads, Memory, and Dataflow Define GPUs and TPUs

✔ Elegant
✔ Feels like a classic systems text

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More Academic / Engineer-Focused

**“Architectures for Deep Learning:

A Systems-Level Primer”**

✔ Safe, formal
✔ Good if used in classes or internal docs

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🚫 Titles to Avoid

• “Introduction to GPUs and TPUs” (too generic)

• “Deep Learning Accelerators Explained” (sounds shallow)

• “Modern AI Hardware” (says nothing)

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2️⃣ How to Add Deep References Without Scaring Beginners

The trick: layered references.

Rule:

Never interrupt the narrative with citations.
Put depth behind collapsible or end-of-chapter references.

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3️⃣ Reference Structure That Works

At the End of Each Chapter, Add:

🔍 “For Readers Who Want to Go Deeper”

Organize references by intent, not by type.

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Example: Chapter 0 References

🔧 Hardware & Architecture

• Hennessy & Patterson — Computer Architecture: A Quantitative Approach
  → Roofline model, memory bottlenecks

• Weste & Harris — CMOS VLSI Design
  → Energy cost of data movement

• Rabaey — Digital Integrated Circuits

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⚙️ AI Accelerators & GPUs

• Jouppi et al. — In-Datacenter Performance Analysis of a TPU

• NVIDIA GPU Architecture Whitepapers

• Kirk & Hwu — Programming Massively Parallel Processors

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🧠 ML Workloads

• Goodfellow et al. — Deep Learning

• Sze et al. — Efficient Processing of Deep Neural Networks

• Vaswani et al. — Attention Is All You Need

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📊 Scaling & Systems

• “How to Scale Your Model” (OpenAI / DeepMind-style blog)

• Goyal et al. — Accurate, Large Minibatch SGD

• Megatron-LM & GPipe papers

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4️⃣ Global “Further Reading” Section (End of Primer)

This is where you earn hardware engineer credibility.

Core Architecture

• Hennessy & Patterson

• ISCA / MICRO / HPCA proceedings

Accelerator Design

• Eyeriss

• Gemmini

• NVDLA

Physical Design & Power

• Chandrakasan — Low Power Digital CMOS

• ISSCC papers

Industry Docs

• NVIDIA CUDA Programming Guide

• Google TPU System Architecture docs

• AMD CDNA whitepapers

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5️⃣ Subtle but Powerful Trick

Add icons to references:

• 🟢 Conceptual

• 🟡 Architecture-level

• 🔴 Circuit / RTL-level

This tells readers how deep each reference goes without words.

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6️⃣ My Strong Recommendation

If you want this to become a canonical reference:

Title:

Compute Is Cheap, Data Is Expensive
A Practical Primer on GPUs, TPUs, and AI Hardware

Structure:

• Story-driven chapters

• End-of-chapter deep dives

• One killer idea per chapter

• No inline citations