🔄 Quick Recap

• Lesson 16: We explored memory bottlenecks — how slow RAM can hold back even the fastest CPUs.

• Lesson 17: We saw how multi-channel RAM widens the highway and how LPDDR saves power in mobile devices.

But what if we need both ultra-wide bandwidth and compact efficiency? That’s where 3D-stacked memory and HBM (High Bandwidth Memory) come in.

🧠 What is High Bandwidth Memory (HBM)?

High Bandwidth Memory (HBM) is a new type of RAM that solves bandwidth bottlenecks by stacking multiple memory chips vertically and connecting them directly to the CPU or GPU with extremely wide pathways.

👉 Analogy:

• Normal RAM = many houses spread across a city with narrow roads.

• HBM = a skyscraper with super-wide elevators.

Instead of data traveling across a long highway, it flows through short, fat pipelines.

🏗️ How 3D Stacked Memory Works

In normal DDR RAM:

• Chips are laid side by side on DIMM sticks.

• Data travels through a memory bus across the motherboard.

In HBM:

• Chips are stacked vertically (like pancakes).

• They are connected using TSVs (Through-Silicon Vias) → microscopic vertical wires drilled through the silicon.

• The whole stack sits right next to the CPU/GPU on a silicon interposer (a thin base layer that connects everything).

👉 Instead of long wires = short, dense, vertical tunnels.

⚡ Why HBM is So Fast

1. Wide Bus

   • DDR4 = 64-bit bus.

   • HBM = up to 1024-bit wide bus per stack.

   • This makes the data highway massively wider.

2. Short Distance

   • Normal RAM is on DIMM sticks far from CPU.

   • HBM sits right next to CPU/GPU, reducing latency.

3. Stacking

   • More layers = more capacity in less space.

📊 Example: Bandwidth Comparison

• DDR4-3200 (dual channel): ~51 GB/s.

• DDR5-6400 (dual channel): ~102 GB/s.

• HBM2: Up to 460 GB/s per stack.

• HBM3: Up to 819 GB/s per stack.

👉 That’s nearly 10× faster than DDR5!

🎮 Where is HBM Used?

Graphics Cards (GPUs)

• AMD Radeon Vega GPUs used HBM2.

• NVIDIA A100/A100 GPUs for AI use HBM2e/HBM3.

• GPUs need extreme bandwidth for textures, ray tracing, and parallel workloads.

AI Accelerators 🤖

• AI training (like ChatGPT!) uses GPUs with HBM.

• Faster memory = faster neural network training.

Supercomputers 🌍

• Top supercomputers rely on HBM for high throughput.

• Example: Fugaku supercomputer in Japan uses HBM for petaflop-level performance.