Answers.Body: <p>The topology of a neural network can have a significant impact on the performance of <code>GPU</code> and <code>TPU</code> acceleration.</p>
  <blockquote>
  <p>The most important factor is the <code>number of layers</code> and the <code>connectivity</code> between them.</p>
  </blockquote>
  <p>Simple topologies require less data movement and can be more easily parallelized. a shallower network with fewer layers will often be faster to train on a <code>GPU</code> or <code>TPU</code> than a deeper network with more layers. This is because each layer in a neural network must be fully connected to the previous and next layers, and a deep network will have many more connections than a shallow network.</p>
  <p>Additionally, the activation function used for each layer can also impact performance. <code>ReLU</code> is a common activation function that is often used in networks that are accelerated by <code>GPUs</code> or <code>TPUs</code>.</p>
  <h2>GPU-based acceleration of CNN</h2>
  <ul>
  <li><p><code>GPU</code> is widely used in neural network applications due to a large number of ALU units which helps in faster data processing (multiplication and summation operations in <code>NN</code>), and also the <code>GPU</code> caches, which help in data reuse.</p>
  </li>
  <li><p>The <code>GPU</code> is capable of merging multiple data access requests using the controllers, and it helps in massive parallel and pipelined processing.
  <code>GPU</code> is a temporal architecture paradigm with a large number of <code>ALUs</code>, but the ALUs lack direct data communication, and they communicate using direct memory access.</p>
  </li>
  <li><p><code>GPU</code> has around <span class="math-container">$3,000–5,000$</span> <code>ALU</code> But the Von Neumann bottleneck exists in <code>GPU</code> due to the access to registers and the shared memory for intermediate data storage in every <code>ALU</code> operation.</p>
  </li>
  <li><p>The <code>GPU</code> has specialized libraries for <code>CNN</code> acceleration like <strong><a href="https://arxiv.org/abs/1412.7580" rel="nofollow noreferrer">fbfft</a></strong> (<a href="https://scholar.google.com/scholar_lookup?title=Fast%20convolutional%20nets%20with%20fbfft:%20A%20GPU%20performance%20evaluation&amp;author=Vasilache&amp;publication_year=2015" rel="nofollow noreferrer">Vasilache et al., 2015</a>). While using a high working set, the shared memory cannot be used, and there is a need for global memory access in <code>GPU</code>, and this leads to more memory footprints and memory access.</p>
  </li>
  </ul>
  <h2>TPU-based acceleration of CNN</h2>
  <ul>
  <li><p>TPU is a custom-made <code>ASIC</code> with a <strong>matrix processor</strong> which is specially designed for neural networks. it effectively handles the addition and multiplication in neural nets at a very high speed with very little power consumption.</p>
  </li>
  <li><p>The von Neumann bottleneck in <code>CPU</code> and <code>GPU</code> is overcome in <code>TPU</code> with the <a href="https://en.wikipedia.org/wiki/Systolic_array" rel="nofollow noreferrer">systolic array</a> structure. TPU v2 single processor has <code>16-bit</code> two <code>128 × 128</code> systolic arrays with <code>32,768 ALUs</code>.</p>
  </li>
  <li><p>Systolic array in TPU helps in data reuse which makes the performance high and execution energy efficient in <code>CNN</code>.</p>
  </li>
  <li><p>The two-dimensional multiply unit helps in matrix multiplication faster compared to the one-dimensional multiply units in <code>CPU</code> and <code>GPU</code>.</p>
  </li>
  <li><p>In <code>TPU</code>, eight-bit integers are used in place of the <code>32-bit floating-point</code> operations, and this makes the computations faster and memory efficient.</p>
  </li>
  <li><p>Unlike <code>CPU</code> and <code>GPU</code> <code>TPU</code> drops features that are not used in the neural network, which helps in saving energy.</p>
  </li>
  <li><p>CNN implementation in <code>TPU</code> will have both <code>TPU</code> and <code>CPU</code> usage in parallel to run the linear and non-linear elements in <code>CNN</code>.</p>
  </li>
  <li><p>In <code>CNN</code> the convolution and classification layer is executed in <code>TPU</code>since it is a <a href="https://www.intel.com/content/www/us/en/developer/articles/technical/introducing-batch-gemm-operations.html" rel="nofollow noreferrer">GEMM operation</a>, and the Pooling and Flattening are executed in the <code>CPU</code>.</p>
  </li>
  </ul>
  <hr />
  <blockquote>
  <p>Here is the <strong><a href="https://research.vit.ac.in/publication/effect-of-neural-network-structure-in-accelerating-performance" rel="nofollow noreferrer">paper</a></strong> that analyzed CNN model performance for the three-image processing application in GPU/TPU platforms in Colab for various batch sizes. <strong>The analysis was done by <code>varying</code> the final feed-forward network and the hidden layers, and this gives an inference on how the <code>performance</code> is affected when <code>the model structure changes</code></strong></p>
  </blockquote>
  <p><a href="https://i.sstatic.net/Kfp2S.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/Kfp2S.jpg" alt="one" /></a>
  <code>Fig(a)</code>: <em>Training time for single and multiple convolutional layer networks.</em></p>
  <p>In the paper, analysis was done using a single convolutional layer followed by all other layers for the mask detection application for binary class, and the training time was analyzed for GPU and TPU for both networks. The training time increases when the convolutional layers are removed because the number of nodes gets more, and thereby training time increases. The training time for Single layer convolution and multiple-layer Convolution for different batch sizes is shown in <code>Fig(a)</code>.</p>
  <p><strong>The analysis clearly shows that the time decreases when the number of convolutions increases due to a reduction in the number of nodes. The training time is less for the multiple layers CNN compared to single-layer CNN and also with an increase in the batch size.</strong></p>
  <p><a href="https://i.sstatic.net/3wxRw.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/3wxRw.jpg" alt="two" /></a>
  <code>Fig(b)</code>: <em>Execution time for multi and binary classes</em>.</p>
  <p>The overall training time for each of the three applications was in GPU and TPU for both binary, and multiple classifications were analyzed and shown in <code>Fig(b)</code>.</p>
  <p>From <code>Fig(b)</code>, <strong>it is clear that compared to <code>TPU</code>, <code>GPU</code> has a low time for execution of the CNN. This occurs due to the bottleneck that occurs in TPU due to the in-between CPU access.</strong></p>
  <blockquote>
  <p><strong>GPU</strong>: GPU performs well for small batches and gives better flexibility and easy programming. For small data, batch sizes GPU fits better due to the execution pattern in wraps and scheduling id easy on-stream multiprocessors. For large dataset and network models, GPU performs well by optimizing memory reuse. In fully connected neural networks, weight reuse is less, so as the model size increases, this leads to high memory traffic. In GPU, the memory bandwidth makes it practical for applications with memory requirements. Large neural networks work better on GPU compared to CPU due to the extra parallelism feature. For fully connected neural networks, GPU works better compared to CPU, but for large batch sizes, TPU performs well.</p>
  </blockquote>
  <blockquote>
  <p><strong>TPU</strong>: TPU performs well on CNN with large batches to give high throughput in training time using the systolic array structure. Large batches of data are needed for the full utilization of the matrix multiply units in the systolic array of TPU. In CNN, the speedup increases with batch size. For enormous batch sizes and complex CNN, TPU is the best because of the spatial reuse characteristics of CNNs. But in fully connected networks, the weight reuse is less, and so TPU is not preferred.</p>
  </blockquote>
  <hr />
  <p>Different neural network topologies may require different amounts of resources (memory, computational power, etc.), which could affect the efficiency of hardware acceleration. There is no easy rule of thumb, however, as it depends on the specific architecture and implementation.</p>
  
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Body: <p>I was thinking about different neural network topologies for some applications. However, I am not sure how this would affect the efficiency of hardware acceleration using GPU/TPU/some other chip.</p>
  <p>If, instead of layers that would be fully connected, I have layers with neurons connected in some other way (some pairs of neurons connected, others not), how is this going to affect the hardware acceleration?</p>
  <p>An example of this is the convolutional networks. However, there is still a clear pattern, which perhaps is exploited by the acceleration, which would mean that if there is no such pattern, the acceleration would not work as well?</p>
  <p>Should this be a concern? If so, is there some rule of thumb for how the connectivity pattern is going to affect the efficiency of hardware acceleration?</p>
  
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Comments.Text: The answer is going to depend on details - e.g. how sparse the connections are, whether they can be arranged meaningfully into layers etc. Also whether you have training data available in large batches. Do you have any specific use case or scenario that would narrow the scope down a little? Or are you looking for a broad but shallow answer?
Comments.UserDisplayName: Neil Slater
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Comments.Text: I am interested in a broad but shallow answer. Or even better, to some place where I could read more.
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Comments.Text: Yes, it matters, and I think you have to manually decide how to divide it up, and hello pre-covid-19 question.
Comments.UserDisplayName: Criticize SE actions means ban
Tags: <neural-networks><convolutional-neural-networks><training><architecture><hardware>
Title: How do neural network topologies affect GPU/TPU acceleration?
UserDisplayName: user2316602
ThreadText: # How do neural network topologies affect GPU/TPU acceleration?
  
  (Asked by: username_0 on 2019-09-23)
  
  I was thinking about different neural network topologies for some applications. However, I am not sure how this would affect the efficiency of hardware acceleration using GPU/TPU/some other chip.
  
  If, instead of layers that would be fully connected, I have layers with neurons connected in some other way (some pairs of neurons connected, others not), how is this going to affect the hardware acceleration?
  
  An example of this is the convolutional networks. However, there is still a clear pattern, which perhaps is exploited by the acceleration, which would mean that if there is no such pattern, the acceleration would not work as well?
  
  Should this be a concern? If so, is there some rule of thumb for how the connectivity pattern is going to affect the efficiency of hardware acceleration?
  
  **Tags:** neural-networks, convolutional-neural-networks, training, architecture, hardware
  
  > **Comments:**
  > **username_1**: The answer is going to depend on details - e.g. how sparse the connections are, whether they can be arranged meaningfully into layers etc. Also whether you have training data available in large batches. Do you have any specific use case or scenario that would narrow the scope down a little? Or are you looking for a broad but shallow answer?  
  > **username_0**: I am interested in a broad but shallow answer. Or even better, to some place where I could read more.  
  > **username_2**: Yes, it matters, and I think you have to manually decide how to divide it up, and hello pre-covid-19 question.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2022-10-29. Score: 2 | Accepted answer
  
  The topology of a neural network can have a significant impact on the performance of `GPU` and `TPU` acceleration.
  
  > The most important factor is the `number of layers` and the `connectivity` between them.
  
  Simple topologies require less data movement and can be more easily parallelized. a shallower network with fewer layers will often be faster to train on a `GPU` or `TPU` than a deeper network with more layers. This is because each layer in a neural network must be fully connected to the previous and next layers, and a deep network will have many more connections than a shallow network.
  
  Additionally, the activation function used for each layer can also impact performance. `ReLU` is a common activation function that is often used in networks that are accelerated by `GPUs` or `TPUs`.
  
  ## GPU-based acceleration of CNN
  
  * `GPU` is widely used in neural network applications due to a large number of ALU units which helps in faster data processing (multiplication and summation operations in `NN`), and also the `GPU` caches, which help in data reuse.
  * The `GPU` is capable of merging multiple data access requests using the controllers, and it helps in massive parallel and pipelined processing.
    `GPU` is a temporal architecture paradigm with a large number of `ALUs`, but the ALUs lack direct data communication, and they communicate using direct memory access.
  * `GPU` has around $3,000–5,000$ `ALU` But the Von Neumann bottleneck exists in `GPU` due to the access to registers and the shared memory for intermediate data storage in every `ALU` operation.
  * The `GPU` has specialized libraries for `CNN` acceleration like **[fbfft](https://arxiv.org/abs/1412.7580)** ([Vasilache et al., 2015](https://scholar.google.com/scholar_lookup?title=Fast%20convolutional%20nets%20with%20fbfft:%20A%20GPU%20performance%20evaluation&author=Vasilache&publication_year=2015)). While using a high working set, the shared memory cannot be used, and there is a need for global memory access in `GPU`, and this leads to more memory footprints and memory access.
  
  ## TPU-based acceleration of CNN
  
  * TPU is a custom-made `ASIC` with a **matrix processor** which is specially designed for neural networks. it effectively handles the addition and multiplication in neural nets at a very high speed with very little power consumption.
  * The von Neumann bottleneck in `CPU` and `GPU` is overcome in `TPU` with the [systolic array](https://en.wikipedia.org/wiki/Systolic_array) structure. TPU v2 single processor has `16-bit` two `128 × 128` systolic arrays with `32,768 ALUs`.
  * Systolic array in TPU helps in data reuse which makes the performance high and execution energy efficient in `CNN`.
  * The two-dimensional multiply unit helps in matrix multiplication faster compared to the one-dimensional multiply units in `CPU` and `GPU`.
  * In `TPU`, eight-bit integers are used in place of the `32-bit floating-point` operations, and this makes the computations faster and memory efficient.
  * Unlike `CPU` and `GPU` `TPU` drops features that are not used in the neural network, which helps in saving energy.
  * CNN implementation in `TPU` will have both `TPU` and `CPU` usage in parallel to run the linear and non-linear elements in `CNN`.
  * In `CNN` the convolution and classification layer is executed in `TPU`since it is a [GEMM operation](https://www.intel.com/content/www/us/en/developer/articles/technical/introducing-batch-gemm-operations.html), and the Pooling and Flattening are executed in the `CPU`.
  
  ---
  
  > Here is the **[paper](https://research.vit.ac.in/publication/effect-of-neural-network-structure-in-accelerating-performance)** that analyzed CNN model performance for the three-image processing application in GPU/TPU platforms in Colab for various batch sizes. **The analysis was done by `varying` the final feed-forward network and the hidden layers, and this gives an inference on how the `performance` is affected when `the model structure changes`**
  
  [![one](https://i.sstatic.net/Kfp2S.jpg)](https://i.sstatic.net/Kfp2S.jpg)
  `Fig(a)`: *Training time for single and multiple convolutional layer networks.*
  
  In the paper, analysis was done using a single convolutional layer followed by all other layers for the mask detection application for binary class, and the training time was analyzed for GPU and TPU for both networks. The training time increases when the convolutional layers are removed because the number of nodes gets more, and thereby training time increases. The training time for Single layer convolution and multiple-layer Convolution for different batch sizes is shown in `Fig(a)`.
  
  **The analysis clearly shows that the time decreases when the number of convolutions increases due to a reduction in the number of nodes. The training time is less for the multiple layers CNN compared to single-layer CNN and also with an increase in the batch size.**
  
  [![two](https://i.sstatic.net/3wxRw.jpg)](https://i.sstatic.net/3wxRw.jpg)
  `Fig(b)`: *Execution time for multi and binary classes*.
  
  The overall training time for each of the three applications was in GPU and TPU for both binary, and multiple classifications were analyzed and shown in `Fig(b)`.
  
  From `Fig(b)`, **it is clear that compared to `TPU`, `GPU` has a low time for execution of the CNN. This occurs due to the bottleneck that occurs in TPU due to the in-between CPU access.**
  
  > **GPU**: GPU performs well for small batches and gives better flexibility and easy programming. For small data, batch sizes GPU fits better due to the execution pattern in wraps and scheduling id easy on-stream multiprocessors. For large dataset and network models, GPU performs well by optimizing memory reuse. In fully connected neural networks, weight reuse is less, so as the model size increases, this leads to high memory traffic. In GPU, the memory bandwidth makes it practical for applications with memory requirements. Large neural networks work better on GPU compared to CPU due to the extra parallelism feature. For fully connected neural networks, GPU works better compared to CPU, but for large batch sizes, TPU performs well.
  
  > **TPU**: TPU performs well on CNN with large batches to give high throughput in training time using the systolic array structure. Large batches of data are needed for the full utilization of the matrix multiply units in the systolic array of TPU. In CNN, the speedup increases with batch size. For enormous batch sizes and complex CNN, TPU is the best because of the spatial reuse characteristics of CNNs. But in fully connected networks, the weight reuse is less, and so TPU is not preferred.
  
  ---
  
  Different neural network topologies may require different amounts of resources (memory, computational power, etc.), which could affect the efficiency of hardware acceleration. There is no easy rule of thumb, however, as it depends on the specific architecture and implementation.
  
  
  ---
  
  