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Other architectures such as the [[Cell microprocessor]] have exhibited features significantly overlap with AI accelerators (support for packed low precision arithmetic, dataflow architecture, throughput over latency). One or more [[DSP]]s have also been used as neural network accelerators. The [[Physics processing unit]] was yet another example of an attempt to fill the gap between [[CPU]] and GPU in PC hardware, however physics tends to require 32bit precision and up, whilst much lower precision is optimal for AI.
Vendors of graphics processing units saw the opportunity and generalised their pipelines with specific support for [[GPGPU]] (which killed off the market for a dedicated physics accelerator, and superseded Cell in video game consoles, and led to their use in implementing [[convolutional neural network]]s such as [[AlexNet]]), as such as of 2016 most AI work is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
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.
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