2021|谷歌董事会主席John Hennessy：AI技术发展放缓，我们正处于半导体产业寒冬 | 钛媒体T-EDGE( 八 )
So this just shows you an example so you get an idea of how were using silicon rather differently in these environments then we would in a traditional processor.
这里我们来看一个例子，以便了解这些处理器与常规处理器的不同之处 。
What I've done here is taken a first generation TPU-1 the first tensor processing unit from Google but I could take the second or third or fourth the numbers would be very similar. I show you what it looks like it's a block diagram in terms of what the chip area devoted to. There's a very large matrix multiply unit that can do a two 56 x 2 56 x 8 bit multiplies and the later ones actually have floating point versions of that multiplying. It has a unified buffer used for local activations of memory buffer, interfaces accumulators, a little bit of controls and interfaces to DRAM.
这里展示是谷歌的第一代 TPU-1 ，当然我也可以采用第二、第三或第四代，但是它们带来的结果是非常相似的 。这些看起来像格子一样的图就是芯片各区域的分工 。它有一个非常大的矩阵乘法单元，可以执行两个 56 x 2 56 x 8 位乘法，后者实具有浮点版本乘法 。它有一个统一的缓冲区，用于本地内存激活 。还有接口、累加器、DRAM 。
Today that would be high bandwidth DRAMs early on it with DDR3. So if you look at the way in which the area is used. 44% of is used for memory to store temporary results in weights and things been computed. Almost 40% of being used for compute, 15% for the interfaces and 2% for control.
在今天我们使用的是高带宽DRAM，以前可能用的是DDR3 。那我们来具体看看这些区域的分工 。44% 用于内存以短时间内存储运算结果 。40% 用于计算，15% 用于接口，2% 用于控件 。
Compare that to a single Skylake core from an Intel processor. In that case, 33% as being used for cach. So noticed that we have more memory capacity in the TPU then we have on the Skylake core. In fact if you were to remove the caps from the cache that number because that's overhead it's not real data, that number would even be larger. The amount on the Skylake core will probably drop to about 30% also almost 50% more being used for active data.
将其与英特尔的 Skylake架构进行比较 。在这种情况下，33% 用于缓存 。请注意，我们在 TPU 中拥有比在Skylake 核心上更多的内存容量，事实上，如果移除缓存限制，这个数字甚至会更大 。Skylake 核心上的数量可能会下降到大约 30%，用于活动数据的数量也会增加近 50% 。
30% of the area is used for control. That's because the Skylake core is an out of order dynamic schedule processor like most modern general purpose processors and that requires significantly more area for the control, roughly 15 times more area for control. That control is overhead. It’s energy intensive computation unfortunately the control unit. So it's also a big power consumer. 21% for compute.
30% 的区域用于控制 。这是因为与大多数现代通用处理器一样，Skylake 核心是一个无序的动态调度处理器，需要更多的控制区域，大约是15 倍的区域 。这种控制是额外负担 。不幸的是，控制单元是能源密集型计算，所以它也是一个能量消耗大户 。21% 用于计算 。
So noticed that the big advantage that exists here is the compute areas roughly almost double what it is in a Skylake core. Memory management there's memory management overhead and finally miscellaneous overhead. so the Skylake core is using a lot more for control a lot less for compute and somewhat less for memory.
这里存在的最大优势是计算区域几乎是 Skylake 核心的两倍 。内存管理有内存管理负担，最后是杂项负担 。因此，控制占据了Skylake 核心的区域，意味着用于计算的区域更少了，内存也是同理 。
So where does this bring us? We've come to an interesting time in the computing industry and I just want to conclude by reflecting on this and how saying something about how things are likely to go forward in the future because I think we're at a real turning point at this point in the history of computing.
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