CPU vs GPU vs TPU
The difference between CPU, GPU and TPU is that the CPU handles all the logics, calculations, and input/output of the computer, it is a general-purpose processor. In comparison, GPU is an additional processor to enhance the graphical interface and run high-end tasks. TPUs are powerful custom-built processors to run the project made on a specific framework, i.e. TensorFlow.
Content Overview
- CPU: Central Processing Unit. Manage all the functions of a computer. Central Processing Unit achieves the highest FLOPS utilisation for RNNs and supports the largest model because of large memory capacity.
- GPU: Graphical Processing Unit. Enhance the graphical performance of the computer. Graphics Processing Unit shows better flexibility and programmability for irregular computations, such as small batches and nonMatMul computations.
- TPU: Tensor Processing Unit. Custom build ASIC to accelerate TensorFlow projects. Tensor Processing Unit is highly-optimised for large batches and CNNs and has the highest training throughput.
CPU vs GPU vs TPU
| CPU | GPU | TPU |
| Several core | Thousands of Cores | Matrix based workload |
| Low latency | High data throughput | High latency |
| Serial processing | Massive parallel computing | High data throughput |
| Limited simultaneous operations | Limited multitasking | Suited for large batch sizes |
| Large memory capacity | Low memory | Complex neural network models |
CPU – Central Processing Unit
• Central Processing Unit abbreviation CPU, is the electronic circuitry, which work as a brains of the computer that perform the basic arithmetic, logical, control and input/output operations specified by the instructions of a computer program.
• CPU can handle tens of operation per cycle
• A processor designed to solve every computational problem in a general fashion. The cache and memory design is designed to be optimal for any general programming problem
GPU – Graphics processing unit
• GPU, the Graphics Processing Unit is a specialized electronic circuit designed to render 2D and 3D graphics together with a CPU. GPU also known as Graphics Card in the Gamer’s culture. Now GPU are being harnessed more broadly to accelerate computational workloads in areas such as financial modeling, cutting-edge scientific research, deep learning, analytics and oil and gas exploration etc.
• GPU can handle tens of thousands of operation per cycle
• A processor designed to accelerate the rendering of graphics.
TPU – Tensor Processing Unit
• Tensor Processing Unit abbreviation TPU is a custom-built integrated circuit developed specifically for machine learning and tailored for TensorFlow, Google’s open-source machine learning framework. TPU’s have been powering Google data centers since 2015, however Google still uses CPUs and GPUs for other types of machine learning.
• TPU can handle upto 128000 operations per cycle
• A co-processor designed to accelerate deep learning tasks develop using TensorFlow (a programming framework); Compilers have not been developed for TPU which could be used for general purpose programming; hence, it requires significant effort to do general programming on TPU
Which is better TPU or GPU?
GPUs and TPUs both have their own advantages and disadvantages. A single GPU can process thousands of tasks at once, but GPUs are typically less efficient in the way they work with neural networks than a TPU. TPUs are more specialized for machine learning calculations and require more traffic to learn at first, but after that, they are more impactful with less power consumption.
Is TPU faster than CPU?
TPUs are 3x faster than CPUs and 3x slower than GPUs for performing a small number of predictions.
How much faster is TPU vs GPU?
The TPU is 15 to 30 times faster than current GPUs.