This article highlights the evolution and role of semiconductor technology leading the AI ​​era. It also explores the co-evolution of AI and semiconductors, from AI-specific chips like GPUs, TPUs, and NPUs to neuromorphic and quantum computing.

As the AI ​​industry grows, there are increasing instances of big tech companies developing their own AI semiconductors to be installed in data centers. As the enterprise market for operating IT infrastructure expands, the number of companies developing new NPUs is also increasing.
A representative company is Canada's Tenstorrent, led by 'semiconductor design legend' Jim Keller, which is attracting attention in the AI ​​hardware accelerator market, and in Korea, FuriosaAI and Rebellions are developing NPU chips specialized in inference.
--- From "01_The Emergence of AI and the Evolution of Semiconductors"

The performance of AI accelerators is closely tied to the limitations of memory bandwidth. The Von Neumann architecture, a traditional three-tiered computer architecture consisting of a CPU, main memory, and input/output devices, suffers from a bottleneck caused by data transfer speed limitations between memory and computing devices, known as the Von Neumann bottleneck.
In AI model computations that require large-scale data processing, bottlenecks can significantly reduce computational efficiency.
--- From "03_A Look Inside the AI ​​Accelerator"

Advanced packaging technologies are evolving from conventional two-dimensional (2D) to 2.5D and 3D technologies, playing a crucial role in increasing chip integration and improving power efficiency.
2.5D packaging technology is a method of horizontally arranging and connecting multiple chips using a silicon interposer.
This technology is effective in increasing data transmission speed and reducing power consumption by optimizing connections between chips.
For example, NVIDIA's GPU accelerators achieve high performance by horizontally connecting the GPU and multiple high-bandwidth memories (HBM) through an interposer using 2.5D packaging technology to facilitate data transfer.
3D packaging technology stacks chips vertically, enabling direct connections between chips using through-silicon vias (TSVs).
A representative example of the application of 3D packaging technology is HBM.
--- From "06_AI Semiconductor Manufacturing Process and Heterogeneous Integration"

Advances in AI semiconductor technology are expected to improve the performance and energy efficiency of AI systems.
For example, neuromorphic chip technology can perform complex AI tasks with very low power consumption by mimicking the structure of the human brain, and quantum computing technology, when combined with AI, will be able to efficiently process complex problems that are difficult to solve even with current supercomputers.
These technological advancements could increase the scale and complexity of AI models while reducing energy consumption, potentially enabling the construction of more sustainable AI systems.

--- From "09_Future Semiconductor Technology and AI"