Wednesday, January 17, 2018

Why GPU computing and Deep Learning are a match made in heaven?

Deep learning is a branch of machine learning that uses multi-layered neural networks for solving a number of challenging AI problems.

GPU (Graphics Processing Unit) architecture are fundamentally different from CPU architecture. A GPU chip would be significantly slower that a CPU, but a single GPU might have thousands of cores while a CPU usually has not more than 12 cores.

Hence any task that can be parallelized over multiple core is a perfect fit for GPUs. Now it so happens, that deep learning algorithms involve a lot of matrix multiplications that are an excellent candidate for parallel processing over the cores of a GPU. Hence GPUs make deep learning algorithms run faster by an order of magnitude. Even training of models is much faster and hence you can expedite the GTM of your AI solutions.

An example of how GPUs are better for AI solutions is considering AlexNet, a well known image classification deep network. A modern GPU costing about $1000 takes 2.5 days to fully train AlexNet on the very large ImageNet dataset. On the otherhand, it takes a CPU costing several thousand dollars nearly 43 days.

A good video demonstrating the difference between GPU and CPU is here:

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