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Tensor Processing Unit
This chapter introduces the Tensor Processing Unit (TPU), a special chip
developed at Google for ultra-fast execution of neural network mathematical
operations. Similarly to Graphic Processing Units (GPUs), the idea here is to have
a special processor focusing only on very fast matrix operations with no support
for all the other operations normally supported by Central Processing Units (CPUs).
However, the additional improvement with TPUs is to remove from the chip any
hardware support for graphics operation normally present in GPUs (rasterization,
texture mapping, frame buffer operations, and so on). Think of a TPU as a special
purpose co-processor specialized for deep learning, being focused on matrix or
tensor operations. In this chapter we are going to compare CPUs and GPUs with
the three generations of TPUs and Edge TPUs. All these accelerators are available
as November 2019. The chapter will include code examples of using TPUs. So with
that, let's begin.
C/G/T processing units
In this section we discuss CPUs, GPUs, and TPUs. Before discussing TPUs, it will
be useful for us to review CPUs and GPUs.
CPUs and GPUs
You are probably somewhat familiar with the concept of a CPU, a general-purpose
chip sitting in each computer, tablet, and smartphone. CPUs are in charge of all
of the computations: from logical controls, to arithmetic, to register operations,
to operations with memory, and much more. CPUs are subject to the well-known
Moore's law [1], which states that the number of transistors in a dense integrated
circuit doubles about every two years.
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