Conv2D FLOP & MAC Calculator
Estimate the multiply-accumulate operations (MACs) and floating point operations (FLOPs) for Conv2D layers in neural networks based on layer configuration.
What this calculator is doing
This tool computes the computational cost of Conv2D layers in Convolutional Neural Networks.
MACs (Multiply-Accumulate Operations)
Each output element in a Conv2D operation requires a number of MACs based on:
\[
\text{MACs} = H_{\text{out}} \cdot W_{\text{out}} \cdot C_{\text{in}} \cdot K_H \cdot K_W \cdot C_{\text{out}}
\cdot \text{Batch} \cdot \text{Layers}
\]
Where:
- \(H_{\text{out}}, W_{\text{out}}\): Output height and width
- \(C_{\text{in}}\): Input channels
- \(K_H, K_W\): Kernel height and width
- \(C_{\text{out}}\): Number of filters
- Multiply by 2 to get FLOPs (1 MAC = 2 FLOPs)
This is a critical metric for optimizing model deployment on edge devices, GPUs, and TPUs.
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