The article further predicts that within a decade, AI will require a 1-trillion-transistor GPU, necessitating further advancements in semiconductor technology. It discusses the concept of system-technology co-optimization, where different parts of a GPU are built using the best performing and most economical technologies. The article also mentions the need for a common language for 3D chip design, similar to the Mead-Conway moment for integrated circuits in 1978. It concludes by stating that while semiconductor technology development will become more challenging, it also opens up more possibilities for AI advancement.
Key takeaways:
- The advancement of artificial intelligence has been largely enabled by innovations in machine-learning algorithms, the availability of massive data, and progress in energy-efficient computing through the advancement of semiconductor technology.
- AI applications are demanding more from the semiconductor industry, with a need for a 1-trillion-transistor GPU within a decade.
- Integration in semiconductor technology has risen to a new level, moving beyond 2D scaling into 3D system integration, which involves putting together many chips into a tightly integrated, massively interconnected system.
- Energy-efficient performance (EEP) of server GPUs has been improving steadily, and this trend is expected to continue, driven by innovations in materials, device and integration technology, circuit design, system architecture design, and more.