If you have a convolutional neural network with a

224x224x3 input layer,

convolutional layer with 3x3 filters (padding to keep image size the same, stride 1) and output size of 224x224x32 ,

pooling layer of output size 112x112x32 , and a

fully connected layer of output size 1x1x9 ,

assuming batch size of 1, how much memory (where memory is defined as number of values) does it use for forward activations (include the image)?

Question

If you have a convolutional neural network with a

224x224x3 input layer,

convolutional layer with 3x3 filters (padding to keep image size the same, stride 1) and output size of 224x224x32 ,

pooling layer of output size 112x112x32 , and a

fully connected layer of output size 1x1x9 ,

assuming batch size of 1, how much memory (where memory is defined as number of values) does it use for forward activations (include the image)?

BlackTom AI · Accepted Answer

We need to compute the total number of numbers (values) stored for forward activations across all layers, including the input image, for a batch size of 1.
First, determine the size of the input activation: the image is 224 x 224 x 3 channels.
- Input activations: 224 * 224 * 3 = 50,176 * 3 = 150,528 values.
Next, the convolutional layer uses 3x3 filters, padding to keep the spatial dimensions the same, stride 1, and outputs 224 x 224 x 32 feature maps.
- Convolution output activations: 224 * 224 * 32 = 50,176 * 32 = 1,605,632 values.
Then, the pooling layer reduces the spatial size but keeps the same number of feature maps: 112 x 112 x 32.
- Pooling output activations: 112 * 112 * 32 = 12,544 * 32 = 401,408 values.
Finally, the fully connected layer outputs 1 x 1 x 9, i.e., 9 values.
- Fully connected output activations: 1 * 1 * 9 = 9 values.
Now, sum all activations that must be stored for forward propagation (the image, conv output, pooling output, and fc output):
- Total = 150,528 + 1,605,632 + 401,408 + 9 = 2,157,577 values.
If any step in a real framework reuses buffers or does in-place operations, the actual peak memory might differ, but under the straightforward accounting of forward activations stored per layer, the result is 2,157,577 values.
Note: The number you provided (1,774,449) differs from this calculation. Potential reasons for discrepancy could include not counting the input image in some conventions, counting only unique buffers after in-place operations, or a miscalculation in one of the layer outputs. For clarity, this step-by-step adds every activation tensor explicitly as above.

CS-7643-O01, OAN, OSZ Quiz #3: Convolutional Neural Network Architectures (Lesson 6), Visualization (Lesson 7), Advanced Computer Vision Architectures (Lesson 8)

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