AI Semiconductors (2): Factors Determining Performance

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In the previous post, we introduced the basics of AI semiconductors and started discussing factors that influence their performance. Today, let’s dive deeper into what truly determines the performance of AI semiconductors and explore how NVIDIA became the dominant force in this field. If you missed the first post, you can find it here:
👉 AI Semiconductors (1): The Secrets of Their Features and Performance

What Determines the Real Performance of AI Semiconductors?

The real-world performance of an AI semiconductor is determined by the total time it takes to process data from input to computation and finally to output. Simply put, even if a single component excels in one area, if it isn’t compatible with the rest of the system and fails to reduce total processing time, it cannot be considered superior.

This is precisely why NVIDIA dominates the AI semiconductor market—its ability to optimize the entire workflow rather than focusing on isolated components.

1. HBM and Its Role in AI Semiconductors

Let’s revisit High Bandwidth Memory (HBM). While memory semiconductors are manufactured according to international standards set by JEDEC, the real question is not whether they meet these standards but whether they are compatible with NVIDIA’s products, as NVIDIA dominates the GPU market.

Even if an HBM module offers outstanding specifications, if it causes overheating, errors, or delays when paired with NVIDIA GPUs, its overall performance suffers. This is why many companies prioritize passing NVIDIA’s quality tests.

Key Challenge: Heat Generation

HBM must handle massive amounts of data at ultra-high speeds, leading to significant heat generation. Developing solutions to efficiently dissipate heat is one of the most pressing challenges for HBM manufacturers.

2. Communication Technologies: Stability and Precision Over Speed

Another critical factor is the communication technology that connects GPUs to memory. While creating physically robust semiconductors is one aspect, managing 4096 channels transmitting 3276 GB of data per second with precision is an entirely different challenge.

The focus here is not just on speed but also on stability and accuracy.
For instance, many controller companies advertise their high data transfer speeds. However, in real-world scenarios, bottlenecks often occur when certain channels fail to deliver data in time, causing delays that offset the benefits of speed.

3. Chiplet Technology: Scaling Beyond Single Chips

Chiplet technology involves connecting multiple smaller chips to function as a unified system. HBM, which stacks DRAM vertically, is a prime example of this technology.

The Core Issue: Chip-to-Chip Communication

While the speed within a single chip is crucial, chip-to-chip communication is often slower. This means the way chips are connected and the efficiency of their communication protocols are just as important as the performance of the individual chips.
Simply increasing the number of chips doesn’t guarantee better performance. The architecture and interconnect design play a decisive role.

4. CUDA: The Power of Software

Beyond hardware, software is a critical factor in the AI semiconductor ecosystem. NVIDIA’s CUDA (Compute Unified Device Architecture) is the primary development platform for AI software on GPUs.

Why Is CUDA So Important?

Using pre-built applications is relatively straightforward, but developing the tools to build these applications is highly complex. CUDA provides developers with the tools they need to create software that runs efficiently on NVIDIA GPUs. Without CUDA, achieving compatibility and efficiency becomes significantly more difficult.

This software ecosystem is a key reason NVIDIA maintains its dominance in the AI semiconductor market.
A simple analogy: If NVIDIA dictates the design of LEGO connectors, every block must conform to that design to work seamlessly.

Why NVIDIA Dominates the Market

To summarize, NVIDIA’s dominance in AI semiconductors can be attributed to two major factors:

  1. Hardware Compatibility: Ensuring that memory, controllers, and other components work seamlessly with NVIDIA GPUs.
  2. Software Ecosystem: Providing developers with tools like CUDA, which make NVIDIA GPUs the default choice for AI applications.

The Future of AI Semiconductors

Despite NVIDIA’s current dominance, the field of AI semiconductors is still young, with immense room for growth and innovation. The potential for groundbreaking advancements remains high, and there is plenty of time to reshape the industry landscape.

AI semiconductors hold the promise of transforming industries such as autonomous vehicles, space exploration, and medical AI, opening up endless possibilities for a better future.


Join the Conversation

What are your thoughts on the future of AI semiconductors? Do you see opportunities to challenge NVIDIA’s dominance? Feel free to share your insights and feedback in the comments below! Together, let’s explore the transformative potential of this exciting field.

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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