Answers.Body: <p>In May 2016 Google announced a custom ASIC which was is specifically built for machine learning<sup><a href="https://en.wikipedia.org/wiki/TensorFlow#DistBelief" rel="nofollow">wiki</a></sup> and tailored for <a href="https://en.wikipedia.org/wiki/TensorFlow" rel="nofollow">TensorFlow</a>. It is using <a href="https://en.wikipedia.org/wiki/Tensor_processing_unit" rel="nofollow">tensor processing unit</a> (TPU) which is a programmable microprocessor designed to accelerate artificial neural networks.</p>
  
  <p><a href="https://web.stanford.edu/group/brainsinsilicon/goals.html" rel="nofollow">NeuroCores</a>, 12x14 sq-mm chips which can be interconnected in a binary tree, see: <a href="https://en.wikipedia.org/wiki/TensorFlow#DistBelief" rel="nofollow">Neurogrid</a>, a supercomputer which can provide an option for brain simulations.</p>
  
  <p><a href="https://en.wikipedia.org/wiki/TrueNorth" rel="nofollow">TrueNorth</a>, a neuromorphic CMOS chip produced by IBM, which has 4096 cores in the current chip, each can simulate 256 programmable silicon "neurons", giving a total of over a million neurons.</p>
  
  <p>Further readings: <a href="https://en.wikipedia.org/wiki/Neuromorphic_engineering" rel="nofollow">Neuromorphic engineering</a>, <a href="https://en.wikipedia.org/wiki/Vision_processing_unit" rel="nofollow">Vision processing unit</a>, <a href="https://en.wikipedia.org/wiki/Category:AI_accelerators" rel="nofollow">AI accelerators</a></p>
  
  <hr>
  
  <p>As a side note, you can always use an FPGA based piece of hardware which you can implement selected genetic algorithm (GA) directly in hardware. For example the <a href="https://en.wikipedia.org/wiki/CoDi#Implementation_in_Hardware" rel="nofollow">CoDi model</a> was implemented in the FPGA based CAM-Brain Machine (CBM)<sup><a href="http://link.springer.com/article/10.1023%2FA%3A1011286308522" rel="nofollow">2001</a></sup>.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2016-08-03T18:37:03.843Z
Answers.LastActivityDate: 2016-08-06T01:22:37.030Z
Answers.LastEditDate: 2016-08-06T01:22:37.030Z
Answers.UserDisplayName: kenorb
Body: <p>I'm interested in hardware implementation of ANNs (artificial neural networks). Are there any popular existing technology implementations in form of microchips which are purpose designed to run artificial neural networks? For example, a chip which is optimised for an application like image recognition or something similar?</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2016-08-03T16:33:18.713Z
Comments.Text: Maybe you could research "SpiNNaker" and "TrueNorth".
Comments.UserDisplayName: Byte Commander
Tags: <image-recognition><hardware>
Title: Are there any microchips specifically designed to run ANNs?
UserDisplayName: kenorb
ThreadText: # Are there any microchips specifically designed to run ANNs?
  
  (Asked by: username_0 on 2016-08-03)
  
  I'm interested in hardware implementation of ANNs (artificial neural networks). Are there any popular existing technology implementations in form of microchips which are purpose designed to run artificial neural networks? For example, a chip which is optimised for an application like image recognition or something similar?
  
  **Tags:** image-recognition, hardware
  > **Comments:**
  > **username_1**: Maybe you could research "SpiNNaker" and "TrueNorth".  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_0 on 2016-08-03. Score: 6 | Accepted answer
  
  In May 2016 Google announced a custom ASIC which was is specifically built for machine learning[wiki](https://en.wikipedia.org/wiki/TensorFlow#DistBelief) and tailored for [TensorFlow](https://en.wikipedia.org/wiki/TensorFlow). It is using [tensor processing unit](https://en.wikipedia.org/wiki/Tensor_processing_unit) (TPU) which is a programmable microprocessor designed to accelerate artificial neural networks.
  
  [NeuroCores](https://web.stanford.edu/group/brainsinsilicon/goals.html), 12x14 sq-mm chips which can be interconnected in a binary tree, see: [Neurogrid](https://en.wikipedia.org/wiki/TensorFlow#DistBelief), a supercomputer which can provide an option for brain simulations.
  
  [TrueNorth](https://en.wikipedia.org/wiki/TrueNorth), a neuromorphic CMOS chip produced by IBM, which has 4096 cores in the current chip, each can simulate 256 programmable silicon "neurons", giving a total of over a million neurons.
  
  Further readings: [Neuromorphic engineering](https://en.wikipedia.org/wiki/Neuromorphic_engineering), [Vision processing unit](https://en.wikipedia.org/wiki/Vision_processing_unit), [AI accelerators](https://en.wikipedia.org/wiki/Category:AI_accelerators)
  
  ---
  
  As a side note, you can always use an FPGA based piece of hardware which you can implement selected genetic algorithm (GA) directly in hardware. For example the [CoDi model](https://en.wikipedia.org/wiki/CoDi#Implementation_in_Hardware) was implemented in the FPGA based CAM-Brain Machine (CBM)[2001](http://link.springer.com/article/10.1023%2FA%3A1011286308522).
  
  
  ---
  
  