钛媒体T-EDGE|谷歌董事会主席John Hennessy： John( 十 ) 福特|改建|泰国|工厂|机器人

软件实体销售等兴起也是我们很多人没有预料到的，但它确实成为了一个关键的驱动因素，这意味着必须要限制可支持的架构数量，因为软件公司不想因为架构数量太多而需要进行大量的移植和验证工作。
And of course the rise in the dramatic growth of the general purpose microprocessor. This is the period in which microprocessor replaced all other technologies, including the largest super computer. And I think it happened much faster than we expected by the mid 80s microprocessor put a series dent in the mini computer business and it was struggling by the by the early 90s in the main from business and by the mid 90s to 2000s really taking a bite out of the super computer industry. So even the supercomputer industry converted from customize special architectures into an array of these general purpose microprocessor. They were just far too efficient in terms of cost and performance to be to be ignored.
当然还有通用微处理器的快速增长。这是微处理器取代所有其他技术的时期，包括最大的超级计算机。我认为它发生的速度比我们预期的要快得多，因为 80 年代中期，微处理器对微型计算机业务造成了一系列影响。到 90 年代初主要业务陷入困境，而到 90 年代中期到 2000 年代，它确实夺走了超级计算机行业的一些市场份额。因此，即使是超级计算机行业，也从定制的特殊架构转变为一系列的通用微处理器，它们在成本和性能方面的效率实在是太高了，不容忽视。
Now we're all of a sudden in a new area where the new era not because general purpose processor is that gonna go completely go away. They going to remain to be important but they'll be less centric to the drive to the edge to the ferry fastest most important applications with the domain specific processor will begin to play a key role. So rather than perhaps so much a horizontal we will see again a more vertical integration between the people who have the models for deep learning and machine learning systems the people who built the OS and compiler that enabled those to run efficiently train efficiently as well as be deployed in the field.
现在我们突然进入了一个新时代。这并不意味着通用处理器会完全消失，它们仍然很重要，但它们将不是驱动行业发展的主力，能够与软件快速联动的特定领域处理器将会逐渐发挥重大作用。因此，我们接下来或许会看到一个更垂直的行业，会看到拥有深度学习和机器学习模型的开发者，与操作系统和编译器的开发者之间更垂直的整合，使他们的程序能够有效运行、有效地训练以及进入实际使用。
Inference is a critical part is it mean when we deploy these in the field will probably have lots of very specialized processors that do one particular problem. The processor that sits in a camera for example that's a security camera that's going to have a very limited used. The key is going to be optimize for power and efficiency in that key use and cost of course. So we see a different kind of integration and Microsoft Google and Apple are all looking at this.
程序推理是一个关键部分，这意味着当我们进行部署时，可能会有很多非常专业的处理器来处理一个特定的问题。例如，位于摄像头中的处理器用途就非常有限。当然，关键是优化功耗和成本。所以我们看到了一种不同的整合方案。微软、谷歌和苹果都在关注这个领域。
The Apple M1 is a perfect example if you look at the Apple M1, it's a processor designed by apple with a deep understanding of the applications that are likely to run on that processor. So they have a special purpose graphics processor they have a special purpose machine learning domain accelerator on there and then they have multiple cores, but even the cores are not completely homogeneous. Some are slow low power cores, and some are high speed high-performance higher power cores. So we see a completely different design approach with lots more codesign and vertical integration.