钛媒体T-EDGE|谷歌董事会主席John Hennessy： John( 六 ) 福特|改建|泰国|工厂|机器人

我们在做的不只是一个应用程序，而是在尝试做一个应用程序领域，比如深度学习，例如像虚拟现实、图形处理。因此，它不同于ASIC，后者设计仅具有一个功能，就例如调制解调器。
It requires more domain specific knowledge. So we need to have a language which conveys important properties of the application that are hard to deduce if we start with a low level language like C. This is a product of codesign. We design the applications and the domain specific processor together and that's critical to get these to to work together.
它需要更多特定领域的知识。所以我们需要一种语言来传达应用程序的重要属性，如果我们从像 C 这样的语言开始就很难推断出这些属性。这是协同设计的产物。我们一起设计应用程序和特定领域的处理器，这对于让它们协同工作至关重要。
Notice that these are not going to be things on which we run general purpose applications. It's not the intention that we take 100 C code. It’s the intention that we take an application design to be run on that particular DSA and we use a domain specific language to convey the information to the application to the processor that it needs to get significant performance improvements.
请注意，这不是用来运行通用软件的。我们的目的不是要能够运行100 个 C 语言程序。我们的目的是让应用程序设计在特定的 DSA 上运行，我们使用特定领域的语言将应用程序的信息传达给处理器，从而获得显着的性能提升。
The key goal here is to achieve higher efficiency both in the use of power and transistors. Remember those are two limiters the rate at which transistor growth is going forward and the issue of power from the lack of Denard scaling. So we're trying to really improve the efficiency of that.
这里的关键目标是在功率和晶体管方面实现更高的效率。请记住，晶体管增长的速度和登纳德缩放定律是两个限制因素，所以我们正在努力提高效率。
Good news? The good news here is that deep learning is a broadly applicable technology. It's the new programming model, programming with data rather than writing massive amounts of highly specialized code. Use data to train deep learning model to detect that kind of specialized circumstance in the data.
有什么好消息吗？好消息是深度学习是一种广泛适用的技术。这是一种新的编程模型，使用数据进行编程，而不是编写大量高度专业化的代码，而是使用数据训练深度学习模型来发现数据中的特殊情况。
And so we have a good target domain here. We have applications which are really demanding of massive amounts of performance increase through which we think there are appropriate domain specific architectures.
所以我们有一个很好的目标域，我们有一些真正需要大量性能提升的应用程序，因此我们认为是有合适的特定领域架构的。
It's important to understand why these domain specific architectures can win in particular there's no magic here.
我们需要弄明白这些特定领域架构的优势。
People who are familiar with the books Dave Patterson and I co-authored together know that we believe in quantitative analysis in an engineering scientific approach to designing computers. So what makes these domain specific architectures more efficient?
熟悉大卫·帕特森和我合著的书籍的人都知道，在计算机设计上，我们信奉遵循工程学方法论的量化分析。那么是什么让这些特定领域架构更高效呢？
First of all, they use a simple model for parallelism that works in a specific domain and that means they can have less control hardware. So for example we switch from multiple instruction multiple data models in a multicore to a single instruction data model. That means we dramatically improve the energy associated with fetching instructions because now we have to fetch one instruction rather than any instructions.