Moore's Law已經被芯片製造工業推到了極限,單個半導體transistor隻有幾個納米(TSMC最小的已經可以做到3nm,R&D應該能做更小,但量產不易)。如果你不理解這個意義,這麽說吧,幾個納米的矽片,是由不到100個矽原子組成的。這麽小的尺寸,使得芯片的設計者,不得不開始考慮以前可以忽略的物理原理(比如量子效應)。
為了生產這麽小尺寸的芯片,需要買ASML最新的13.5nm波長的EUV光刻機,及相應的配套工藝設備(比如刻蝕,鍍膜,檢測和修複等)。這些半導體製造設備,造價高昂(比如,一台ASML EUV定價超過$150million),除了業內幾個巨頭(TSMC,Samsung,Intel,GlobalFoundry等),已經沒有什麽公司可以負擔得起。這導致兩個非常明顯的趨勢: 1. 大部分芯片公司,本身沒有製造能力,必須把自己設計的芯片交給像TSMC這樣的公司代工;2. 高性能的芯片研發進展緩慢,代價高昂。
當Moore's Law開始顯出疲態的時候,另一個領域的進展卻如火如荼,那就是由AI算法引領的智能、低能耗芯片,比如智能手機(apple,andoid),可穿戴電子設備(apple watch),智能家居產品(google nest, amazon alexa),自動駕駛控製芯片(Intel's mobileye)等等。這個其實很好理解,相比人類社會發展的其它方麵,芯片工業的進步其實有點超前太多。現在迫切需要的是把芯片工業的領先技術,推廣到人類社會生活的更多的應用場景中去。而各種小型,低能耗的智能產品,正是推動這一潮流的最佳結合點。
這個潮流,有點類似於曆史上IBM的大型mainframe電腦被小型的桌麵電腦取代的過程。那種動不動價格超過百萬的mainframe computer, 類似於現在的cloud,功能強大,運算能力驚人,唯一的缺點就是不適合大範圍推廣應用。集成的AI芯片,能讓部分cloud功能在小型的電子設備上得到實現;某些特定的cloud功能,還可通過在cloud上運算後,通過互聯網傳回到本地的小型終端。雖然目前這種智能芯片功能相對單一,但假以時日,其發展前景不可限量。
WSJ article: Huang's law is the new Moore's Law, and explains why Nvdia wants ARM
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”Over the last three to five years, machine-learning networks have been increasing by orders of magnitude in efficiency, says Dennis Laudick, vice president of marketing in Arm's machine-learning group. “Now it's more about making things work in a smaller and smaller environment,” he adds. Arm's smallest and most energy-sipping chips, tiny enough to be powered by a watch battery, can now enable cameras to recognize objects in real time.
This movement of AI processing from the cloud to the “edge”—that is, on the devices themselves—explains Nvidia's desire to buy Arm, says Nexar co-founder and CEO Eran Shir. Nvidia has a near monopoly on AI processing in the cloud. But where two years ago, Nexar performed 40% of its data processing in the cloud, Arm-based chips have enabled it to do much more of that processing in mobile devices, and faster, since it doesn't have to be transmitted over the internet first. Today, the cloud is doing only 15% of the work. In addition, some functions, like a vision-based parking assistant, were not even possible until recently, when the chips in phones became much more capable.“