AI Semiconductors (1): The Secrets of Their Features and Performance

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 In recent years, Artificial Intelligence (AI) has transformed our world, and by now, most people have at least heard of it. Whether you are building services powered by AI, investing in related industries, or conducting research for more efficient AI technologies, one thing is clear—AI is shaping both our present and future.

However, the critical technology enabling AI—AI semiconductors—remains a lesser-known topic. Most people might recognize NVIDIA’s GPUs as the leading AI semiconductor, but few understand why these GPUs have earned their top position or what makes them so essential for AI.

This post aims to explain the features AI semiconductors must have and provide a general understanding of their importance, making it accessible even to non-technical readers. Let’s dive in!


AI and GPUs: A Parallel Path

Did you know that GPUs used for AI differ significantly from the ones in your computer? While they share a name and some core concepts, AI-focused GPUs are not designed for graphics processing. Instead, they’re optimized for AI computations. To distinguish them, some call these devices GPGPUs (General Purpose Computing on GPUs). Other companies have developed alternative architectures like TPUs (Tensor Processing Units), NPUs (Neural Processing Units), and IPUs (Intelligence Processing Units).

Although the concept of AI and large language models (LLMs) has existed since the 1950s, their implementation wasn’t feasible until recent advancements in semiconductor technology. This shows how crucial AI semiconductors are in enabling today’s breakthroughs.

Why Are GPUs So Good for AI?

The secret lies in parallel computing.
Imagine CPUs as specialists performing one complex task at a time, like precise multiplication or division. In contrast, GPUs are generalists that can perform many simple operations (e.g., addition) simultaneously, making them perfect for AI tasks that involve massive datasets.

For instance, training LLMs like ChatGPT requires processing enormous amounts of data efficiently. GPUs excel at this because they divide calculations into smaller parts, process them in parallel, and integrate the results quickly. Think of GPUs as a team of workers tackling a massive task together rather than one worker slogging through it alone.

The Foundation of Semiconductors

To process any computation, computers need to represent 0s and 1s. Early computers did this with light bulbs that turned on and off, but modern systems use transistors. The smaller the transistors, the more efficient and powerful a semiconductor becomes.

Here’s why:

  1. Smaller transistors reduce power consumption and heat.
  2. More transistors in the same space increase processing power.

Major companies like Samsung, SK Hynix, and Intel compete to develop smaller and more efficient semiconductors. Today, the industry has reached the nanometer scale, using processes like photolithography to etch circuits with extreme precision.

Challenges of Scaling

As transistors become smaller, new challenges arise:

  • At very small scales, quantum tunneling occurs, where particles pass through barriers they normally shouldn’t.
  • This has led to the development of 3D architectures like FinFETs, which stack components vertically to overcome 2D limitations.

Interestingly, the naming of semiconductor nodes (e.g., 7nm, 5nm) no longer reflects their actual physical size but rather the expected performance improvements.

Memory Bottlenecks in AI Semiconductors

Even with advanced semiconductors, AI chips face a major bottleneck: memory speed.
Here’s a simple analogy: imagine a restaurant with many chefs (processing units) but limited ingredients (data). Even if the chefs work quickly, the restaurant’s output is limited if ingredients arrive slowly or inconsistently.

In AI semiconductors:

  1. Processing speed = the chefs.
  2. Data = the ingredients.

To address this, solutions include:

  1. Reducing the distance between memory and processors.
  2. Increasing memory capacity (e.g., more DRAM).
  3. Expanding bandwidth for faster data transfer.

This has led to innovations like High Bandwidth Memory (HBM), which stacks DRAM vertically and connects it with processors through wide communication pathways.

What’s Next?

As AI semiconductors evolve, memory bottlenecks remain a critical challenge. Solutions like HBM have significantly improved performance, but there’s still much work to be done. Understanding why NVIDIA dominates the AI semiconductor market will also require examining its advancements in memory and architecture—a topic for the next post.

Join the Conversation

Thank you for reading! If you have feedback or would like to contribute additional insights, feel free to share them in the comments. Let’s learn and innovate together!

References

This post was inspired by The AI Semiconductor Revolution by Kwon Soon-Woo, Kwon Se-Jong, and Yoo Ji-Won (Published by Page2Books).

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