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Tensor Processing Unit
On the other hand, Cloud TPU v2 pods released in alpha in 2018 can achieve 11.5
PetaFLOPS; another impressive improvement. As of 2019 both TPU2 and TPU3 are
in production with different prices:
Figure 6: Google announced TPU v2 and v3 Pods in beta at the Google I/O 2019
TPU v3 board has 4 TPU chips, 8 cores, and liquid cooling. Google has adopted
an ultra-high-speed interconnect hardware derived from supercomputer
technology, for connecting thousands of TPUs with very low latency. Each time
a parameter is updated on a single TPU, all the others are informed via a reduceall
algorithm typically adopted for parallel computation. So, you can think about
TPU v3 as one of the fastest supercomputers available today for matrix and tensor
operations with thousands of TPUs inside it.
Edge TPU
In addition to the three generations of TPUs already discussed, in 2018 Google
announced a special generation of TPUs running on the edge. This TPU is
particularly appropriate for Internet of Things (IoT) and for supporting
TensorFlow Lite on mobile and IoT. With this we conclude the introduction to
TPU v1, v2, and v3. In the next section we will briefly discuss performance.
TPU performance
Discussing performance is always difficult because it is important to first define
the metrics that we are going to measure, and the set of workloads that we are
going to use as benchmarks. For instance, Google reported an impressive linear
scaling for TPU v2 used with ResNet-50 [4] (see Figure 7).
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