Answers.Body: <p>They are all the same, which is basically number. Numbers can be represented in many ways, such as with digital technology (current GPU technology) and analog technology, like Mythic AI or IBM analog computing.</p>
  <p>If you are referring to whether a neural block (tensor layer) is using specialized hardware, for example, an attention mask must be using specialized hardware, then no. It's basically the same (they are just numbers that can be represented in many ways, such as bits, qubits, or continuous values from voltage).</p>
  <p>In digital technology, it's basically using floating point operation from IEEE 754 convention. That's why you commonly see the specs of an AI accelerator using FLOPS (Floating Point Operation Per Second).</p>
  <p>While analog technology, the number is not represented digitally from the convention of IEEE 754, it might be using a physical unit such as voltage, or ampere, or even ohm.</p>
  <p>But there is a special case for Neuromorphic computing that using real human neurons, which operated on reward and punishment, the reward system is using chemicals addictive like drugs, while the punishment system simply randomly electrifies the neuron. But I'm still not sure the number interpretability for such technology.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-10-11T13:56:49.540Z
Answers.LastActivityDate: 2024-10-11T13:56:49.540Z
Answers.UserDisplayName: Muhammad Ikhwan Perwira
Body: <p>My understanding is that most if not all of the current models are running on top of basically the same hardware operations.</p>
  <p>That is to say, typically library calls to pre defined gpu routines that are essentially the same across all current models.</p>
  <p>The one exception I imagine would be with tensor chips -- although I'm not too familiar with that.</p>
  <p>Are there any current Machine Learning Models that manipulate gpu hardware differently?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-10-14T15:52:27.237Z
Comments.Text: The scope of this question seems unclear. Are you familiar with the difference between a FG layer, a LSTM layer and a CNN layer? Transformers? Attention?
Comments.UserDisplayName: MSalters
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-10-23T09:26:33.700Z
Comments.Text: @MSalters what I discovered is most models basically are using the same "frameworks" pytorch and tensorflow
Comments.UserDisplayName: stackoverflow909
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-10-24T11:50:57.847Z
Comments.Text: True, but that says nothing about the hardware. Those are high-level software interfaces. At a lower level, the software interface is usually NVidia's CUDA. Again, a software abstraction, not hardware.
Comments.UserDisplayName: MSalters
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-10-27T21:54:59.800Z
Comments.Text: Differently from what?
Comments.UserDisplayName: nbro
Tags: <neural-networks><machine-learning><large-language-models><gpu>
Title: Do any Machine Learning Models Use Hardware Differently?
UserDisplayName: stackoverflow909
ThreadText: # Do any Machine Learning Models Use Hardware Differently?
  
  (Asked by: username_0 on 2024-10-11)
  
  My understanding is that most if not all of the current models are running on top of basically the same hardware operations.
  
  That is to say, typically library calls to pre defined gpu routines that are essentially the same across all current models.
  
  The one exception I imagine would be with tensor chips -- although I'm not too familiar with that.
  
  Are there any current Machine Learning Models that manipulate gpu hardware differently?
  
  **Tags:** neural-networks, machine-learning, large-language-models, gpu
  
  > **Comments:**
  > **username_1**: The scope of this question seems unclear. Are you familiar with the difference between a FG layer, a LSTM layer and a CNN layer? Transformers? Attention?  
  > **username_0**: @username_1 what I discovered is most models basically are using the same "frameworks" pytorch and tensorflow  
  > **username_1**: True, but that says nothing about the hardware. Those are high-level software interfaces. At a lower level, the software interface is usually NVidia's CUDA. Again, a software abstraction, not hardware.  
  > **username_2**: Differently from what?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2024-10-11. Score: 0 | Accepted answer
  
  They are all the same, which is basically number. Numbers can be represented in many ways, such as with digital technology (current GPU technology) and analog technology, like Mythic AI or IBM analog computing.
  
  If you are referring to whether a neural block (tensor layer) is using specialized hardware, for example, an attention mask must be using specialized hardware, then no. It's basically the same (they are just numbers that can be represented in many ways, such as bits, qubits, or continuous values from voltage).
  
  In digital technology, it's basically using floating point operation from IEEE 754 convention. That's why you commonly see the specs of an AI accelerator using FLOPS (Floating Point Operation Per Second).
  
  While analog technology, the number is not represented digitally from the convention of IEEE 754, it might be using a physical unit such as voltage, or ampere, or even ohm.
  
  But there is a special case for Neuromorphic computing that using real human neurons, which operated on reward and punishment, the reward system is using chemicals addictive like drugs, while the punishment system simply randomly electrifies the neuron. But I'm still not sure the number interpretability for such technology.
  
  
  ---
  
  