Answers.Body: <p>In the context of Deeplearning:</p>
  <ul>
  <li>FLOPS: Floating Point Ops per Second</li>
  <li>FLOPs: Floating Point Ops</li>
  </ul>
  <p>FLOPS, refers to the number of floating point operations that can be performed by a computing entity in one second. It is used to quantify the performance of a hardware.</p>
  <p>FLOPs, simply means the total number of floating point operations required for a single forward pass. The higher the FLOPs, the slower the model and hence low throughput.</p>
  <p>This thread on stack overflow might help to get a deeper insight:
  <a href="https://stackoverflow.com/questions/58498651/what-is-flops-in-field-of-deep-learning">https://stackoverflow.com/questions/58498651/what-is-flops-in-field-of-deep-learning</a></p>
  
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Answers.CreationDate: 2021-12-21T20:33:55.367Z
Answers.LastActivityDate: 2021-12-21T20:33:55.367Z
Answers.UserDisplayName: Sooryakiran Pallikulathil
Body: <p>I understand that FLOPS means floating-point operations per second, and throughput is the number of inputs (for example, images) per second. If a model has higher FLOPS, it means it performs faster.</p>
  <p>However, in the article <a href="https://arxiv.org/pdf/2106.01401.pdf" rel="nofollow noreferrer">Container: Context Aggregation Network</a>, they show that:</p>
  <p><a href="https://i.sstatic.net/yrX8N.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/yrX8N.png" alt="enter image description here" /></a></p>
  <p>The container has higher FLOPS and less throughput, while the container-light has lower FLOPS and higher throughput.</p>
  <p>What is the reason for that?</p>
  
Tags: <neural-networks><deep-learning><papers><image-net><flops>
Title: Does higher FLOPS mean higher throughput?
UserDisplayName: BestR
ThreadText: # Does higher FLOPS mean higher throughput?
  
  (Asked by: username_0 on 2021-12-21)
  
  I understand that FLOPS means floating-point operations per second, and throughput is the number of inputs (for example, images) per second. If a model has higher FLOPS, it means it performs faster.
  
  However, in the article [Container: Context Aggregation Network](https://arxiv.org/pdf/2106.01401.pdf), they show that:
  
  [![enter image description here](https://i.sstatic.net/yrX8N.png)](https://i.sstatic.net/yrX8N.png)
  
  The container has higher FLOPS and less throughput, while the container-light has lower FLOPS and higher throughput.
  
  What is the reason for that?
  
  **Tags:** neural-networks, deep-learning, papers, image-net, flops
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-12-21. Score: 5 | Accepted answer
  
  In the context of Deeplearning:
  
  * FLOPS: Floating Point Ops per Second
  * FLOPs: Floating Point Ops
  
  FLOPS, refers to the number of floating point operations that can be performed by a computing entity in one second. It is used to quantify the performance of a hardware.
  
  FLOPs, simply means the total number of floating point operations required for a single forward pass. The higher the FLOPs, the slower the model and hence low throughput.
  
  This thread on stack overflow might help to get a deeper insight:
  <https://stackoverflow.com/questions/58498651/what-is-flops-in-field-of-deep-learning>
  
  
  ---
  
  