Graphcore produced a series of striking images of computational graphs mapped to its \"Intelligent Processing Unit.\" <\/p>\n
The graph compiler builds up an intermediate representation of the computational graph to be scheduled and deployed across one or many IPU devices. The compiler can display this computational graph, so an application written at the level of a machine learning framework reveals an image of the computational graph which runs on the IPU. <\/p>\n
The image below shows the graph for the full forward and backward training loop of AlexNet, generated from a TensorFlow description. <\/p>\n<\/p>\n
Our Poplar graph compiler has converted a description of the network into a computational graph of 18.7 million vertices and 115.8 million edges. This graph represents AlexNet as a highly-parallel execution plan for the IPU. The vertices of the graph represent computation processes and the edges represent communication between processes. The layers in the graph are labelled with the corresponding layers from the high level description of the network. The clearly visible clustering is the result of intensive communication between processes in each layer of the network, with lighter communication between layers. <\/p>\n<\/p>\n
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Zuck That says, Have you ever been on the Internet when you came across a checkbox that says Im not a robot? In this video, I explain how those checkboxes (No CAPTCHA reCAPTCHAs) work as well as why they exist in the first place. I mention CAPTCHA farms briefly, but the idea behind them is [] <\/p>\n
Gaetan Hadjeres and Francois Pachet at the Sony Computer Science Laboratories in Paris created DeepBach, then entered Bachs 352 chorales. The resulting composition is certainly in the style. So why does this work better than some other attempts? <\/p>\n
My friend and Cool Tools business partner Kevin Kelly spoke at TEDSummit about the rapid rise of artificial intelligence. The talk is based on his excellent bestselling book, The Inevitable. The actual path of a raindrop as it goes down the valley is unpredictable, but the general direction is inevitable, says digital visionary Kevin Kelly [] <\/p>\n
Python is immensely popular in the data science world for the same reason it is in most other areas of computingit has highly readable syntax and is suitable for anything from short scripts to massive web services. One of its most exciting, newest applications, however, is in machine learning. You can dive into this booming [] <\/p>\n
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"brain scans" of artificial intelligence processes - Boing Boing<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Graphcore produced a series of striking images of computational graphs mapped to its \"Intelligent Processing Unit.\" The graph compiler builds up an intermediate representation of the computational graph to be scheduled and deployed across one or many IPU devices. Continue reading