AI Brainscans

Graphcore aus Bristol visualisieren künstliche Intelligenzen und Neural Networks: Inside an AI 'brain' - What does machine learning look like? Im Bild oben sieht man AlexNet, ein Deep Neural Network, das 2012 einen Preis für den Durchbruch bei Bilderkennung gewann. (via NewAesthetics)

One aspect all recent machine learning frameworks have in common - TensorFlow, MxNet, Caffe, Theano, Torch and others - is that they use the concept of a computational graph as a powerful abstraction. A graph is simply the best way to describe the models you create in a machine learning system. These computational graphs are made up of vertices (think neurons) for the compute elements, connected by edges (think synapses), which describe the communication paths between vertices.

Unlike a scalar CPU or a vector GPU, the Graphcore Intelligent Processing Unit (IPU) is a graph processor. A computer that is designed to manipulate graphs is the ideal target for the computational graph models that are created by machine learning frameworks.

We’ve found one of the easiest ways to describe this is to visualize it. Our software team has developed an amazing set of images of the computational graphs mapped to our IPU. These images are striking because they look so much like a human brain scan once the complexity of the connections is revealed – and they are incredibly beautiful too.

Mehr bei Wired: 'AI brain scans' reveal what happens inside machine learning.

full training graph for Microsoft Research ResNet-34 architecture hosted on Graphcore's IPU from December 2016. The image is coloured to highlight the density of computation resulting the glowing centre in the convolutional layers of the graph

The ResNet architecture is used for building deep neural networks for computer vision and image recognition. The image shown here is the forward (inference) pass of the ResNet 50 layer network used to classify images after being trained using the Graphcore neural network graph library

Resnet 50: deep neural network, A graph processor such as the IPU is designed specifically for building and executing computational graph networks for deep learning and machine learning models of all types. What’s more, the whole model can be hosted on an IPU. This means IPU systems train machine learning models much faster than, and deploy them for inference or prediction much more efficiently than other processors which were simply not designed for this new and important workload. Machine learning is the future of computing and a graph processor like the IPU is the architecture that will carry this next wave of computing forward.

The AlexNet image classification training architecture from November 2016. The vertices in the final three layers of AlexNet are coloured while the rest of the graph is in black and white

An image of the ResNet-34 forward pass used for image recognition. The graph visually shows where multiple images are sent through the network in parallel. This is known as batching

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