Accelerated Deep Learning on GPUs: From Large Scale Training to Embedded Deployment

Time is critical when developing and training the the right network on real-world datasets. GPUs offer a great computational advantage for training deep networks. Newly developed hardware resources, such as the DIGITS DevBox, can greatly reduce this development time.

Using multiple GPUs at the same time allows both multi-GPU training and parallel network model tuning to accelerate training. The four Titan X GPUs in the DIGITS DevBox allow rapid development in a single deskside appliance. Libraries are key to maximizing performance when exploiting the power of the GPU. CUDA libraries, including the NVIDIA cuDNN deep learning library, are also used to accelerate training. cuDNN is an optimized library comprised of functions commonly used to create artificial neural networks. This library includes activation functions, convolutions, and Fourier transforms, with calls catered to processing neural network data. This library is designed with deep neural network frameworks in mind and is easily used with popular open source frameworks such as Torch, Caffe, and Theano.

A trained deep neural network learns its features through an iterative process, convolving abstracted features to discern between the objects it has been trained to identify. NVIDIA DIGITS allows researchers to quickly and easily visualize the layers of trained networks. This novel visualization tool can be used with any GPU accelerated platform and works with popular frameworks like Caffe and Torch. Working with popular frameworks enables easy deployment of trained networks to other platforms, such as embedded platforms. This demonstration will show how easy it is to quickly develop a trained network using multiple GPUs on the DIGITS DevBox with the DIGITS software and deploy it to the newly released embedded platform for classification on a mobile deployment scenario.