AI Semiconductors, the Game Changer of the AI Era

As AI permeates services and industries as a whole, users come to expect fast and stable operation. Models requiring massive computation, like LLMs, need more than just intelligence—they must be backed by semiconductors that deliver results without delay.

AI semiconductors are chips designed to process AI computation quickly and efficiently. Beyond mere computational performance, how fast massive amounts of data can be fetched and processed matters as well, so AI semiconductors can be understood as AI-dedicated accelerators aimed at both data processing and large-scale computation at the same time.

The core computation of AI models is Matrix Operation. As models grow larger, the amount of computation increases exponentially, making it difficult for traditional CPUs alone to respond efficiently.

• CPU: Handles diverse tasks flexibly, but is typically optimized for serial processing based on 1 to 8 cores, making it prone to bottlenecks in large-scale AI computation.
• GPU: Strong at parallel processing with hundreds to thousands of cores, making it suitable for large-scale training.
• NPU: An AI-dedicated semiconductor tailored to neural network structures and AI computation flow, with particular strengths in the Inference stage and on-device AI.

GPUs were originally auxiliary devices for multimedia tasks like gaming and video editing, but since the spread of AI, they have established themselves as the core device for large-scale model training. However, in the actual service stage after training, lower latency and more sophisticated optimization are needed, which has increased the importance of NPUs designed specifically for AI.

The reason the AI semiconductor market is growing so rapidly is that AI is replacing existing CPU-centered tasks across code writing, logical reasoning, communications, video processing, and sensor interpretation. As a result, the computational structure across industries as a whole is shifting toward being AI semiconductor-centered.

At the same time, as AI models become more sophisticated, infrastructure costs and power consumption also grow, making AI efficiency a core challenge. The solution drawing attention here is lightweight algorithms (Model Compression).

Model compression is a method of reducing redundancy (Redundancy) to lower power and memory access at the service stage. As MIT's Lottery Ticket Hypothesis suggests, a huge model may already have a minimal core computation path hidden within it, and finding this can significantly compress the entire model.

However, compressed computation structures don't fit well with existing GPUs. GPUs are highly efficient when performing large volumes of similar operations simultaneously, but with compressed models the computation pattern becomes irregular, which can actually slow things down. So what's needed isn't simply an algorithm change, but a semiconductor structure optimized for compression.

Future AI is likely to evolve in a direction where it judges and plans on its own and moves on to the next action, like agentic AI. In this environment, different computations—language, image, voice, code—are required simultaneously, so the limits of existing GPUs, which are strong at processing similar tasks all at once, become apparent.

Ultimately, AI semiconductors need to evolve into more flexible and adaptive structures. Fast inference, low power, compression-friendliness, and an architecture that can handle diverse tasks together will determine next-generation competitiveness.