Semiconductors and Artificial Intelligence
“High-performance chips are the unsexy, and often unsung, heroes of each computer revolution. They are at the literal core of our desktops, laptops, smartphones and tablets, but for that reason they remain largely hidden to the end user. But from an economic and security perspective, building these chips is a very big deal.
Each era of computing requires different kinds of chips. When desktops reigned supreme, chipmakers sought to maximize processing speed and graphics on a high-resolution screen, with far less concern about power consumption. (Desktops were, after all, always plugged in.) Intel mastered the design of these chips and made billions in the process. But with the advent of smartphones, demand shifted toward more efficient use of power, and Qualcomm, whose chips were based on designs by the British firm ARM, took the throne as the undisputed chip king.
Now, as traditional computing programs are displaced by the operation of AI algorithms, requirements are once again shifting. Machine learning requires the rapid-fire execution of complex mathematical formulas, something for which neither Intel’s nor Qualcomm’s chips are built. Into the void stepped Nvidia, a chipmaker that had previously excelled at graphics processing for video games. The math behind graphics processing aligned well with the requirements for AI, and Nvidia became the go-to player in the chip market,
These chips are central to everything from facial recognition to self-driving cars, and that has set off a race to develop the next-generation AI chip. Google and Microsoft – companies that had long avoided building their own chips – have jumped into the fray, alongside Intel, Qualcomm, and a batch of well-funded Silicon Valley chip start-ups.”
AI Superpowers, by Kai-fu Lee, p 96
My note: In addition to the implications for wafer equipment manufacturers, the enormous data required for AI has implications for storage chips. |
