Content deleted Content added
Line 18:
<ref>{{cite web|title=imagenet classification with deep convolutional neural networks|url=https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf}}</ref>
As such, as of 2016 most AI work is done on these. However at least a factor of 10 in efficiency<ref>{{cite web|title=google boosts machine learning with TPU|url=http://techreport.com/news/30155/google-boosts-machine-learning-with-its-tensor-processing-unit}}mentions 10x efficiency</ref>
can still be gained with a more specific design.
The [[memory access pattern]] of AI calculations differs from graphics, with more a more predictable but deeper [[dataflow]] ,rather than 'gather' from texture-maps & 'scatter' to frame buffers. GPUs devote silicon to dealing with irregular [[scatter-gather memory access patterns]] and [[texture filtering]] for graphics rendering, which isn't needed in AI.
As of 2016, vendors are pushing their own terms, in the hope that their designs and [[API]]s will dominate. In the past after [[graphics accelerator]]s emerged, the industry eventually adopted [[NVidia]]s self assigned term "[[GPU]]" as the collective noun for "graphics accelerators", which had settled on an overall pipeline patterned around [[Direct3D]]. There is no consensus on the boundary between these devices, nor the exact form they will take, however several examples clearly aim to fill this new space.
| |||