Why do we need GPU for Deep Learning?
The current era started to move towards Artificial Intelligence, which massively impacted the world with its ability to achieve the tasks that were a dream of humanity. All of these achievements are mainly due to the research and development in the field of Deep Learning and Neural Network, which are a part of Artificial Intelligence.
What is the GPU?
A graphics processing unit or GPU is a single-chip processor designed for the parallel processing that can be used to accelerate a wide variety of tasks such as video rendering, gaming and machine learning.
A GPU is designed for handling specialized computations, whereas a CPU (Central Processing Unit) is designed for general computations.
Some of the GPU’s:
Why do we need more hardware for Deep Learning?
Deep Learning uses an artificial neural network for a variety of tasks such as image recognition, object detection, image segmentation, and many more. While training, these deep neural networks require a lot of computing power, especially GPU.
Dataset: A deep neural network needs a huge amount of dataset to tune billions of parameters to get good performance. The size of the training dataset is directly proportional to the training time. This training time can be reduced from weeks to days and days to hours by using taking the advantage of parallel processing of a GPU.
Memory: The GPU contains memory called VRAM (Video Random Access Memory). While training a neural network, we provide the dataset in the form of batches. These size of these batches depends upon the amount of VRAM present in the GPU, so a high VRAM GPU can support the high batch size and thus reducing the time taken for training. The higher batch size also helps the neural network to generalize well on the given dataset.
Parallelism: The deep neural network are structured in a uniform manner, each layer consists of thousands of identical artificial neurons performing the same operations. Therefore the structure of the deep neural network can work quite well with the kinds of computation that a GPU can efficiently perform.
Time: A smaller neural network with a small dataset can be easily trained on a CPU. While a deep neural network with billions of parameters and huge dataset would take weeks or months to train on a CPU. This time can be easily reduced by training the neural network on a GPU instead of a CPU.
CPU can be used to train the model where the data size is very small. GPU’s are an excellent choice for training a model with the medium or large dataset for a longer time. CPU can train a deep learning model quite slowly, while GPU accelerates the training process.
Hence, GPU is a better choice to train the Deep Learning Model efficiently and effectively.