## 3 Methodology
### Model Architecture
#### 3.1.1 The Distributed Backbone Model  
As mentioned in [10], the core of the ENN model is a distributed backbone model that acts similarly to a cortical column of neurons that follow a near-identical structure. The main idea of a distributed backbone model is to learn per partitioned dataset. In this study, the initial dataset was divided to have the same number of classes (design rights) as much as possible. There is no overlap in design rights between different distributed backbone models. Distributed neural networks can be trained independently and quickly in parallel.  
Suppose there is a set of 50,000 image data and 100 design rights. Each design right is associated with 500 image data. Suppose we segment the 100 design rights into five masterclasses, each class having 20 design rights. Then, the first model can learn with 10,000 images corresponding to the 1 st-20 th classes, and the second model can learn with 10,000 images corresponding to the 21 st-40 th classes. Likewise, the rest of the models perform the distributed training individually by dividing the data by 10,000 images each.  
Figure 3: Comparison of the structure of the previous model and ENN model when additional training is needed.  
The main reason for learning per divided dataset is to overcome the inefficiency of the existing methodologies that typically train on one huge neural network. The single network model incurs a significant cost of transfer learning on the entire data. Existing methods train one huge neural network to learn all classes, and additional training requires transfer learning over the whole data even when new data are incrementally added. However, our ENN model can be trained on the entire class much quicker by training only the distributed backbone model affected by the change to the dataset. We can even benefit from parallelism by simultaneously training the required distributed backbone models on separate devices.  
Assume a hyperscale neural network that needs to be trained to classify an input into more than 500,000 design rights. If there are 500 images per design right, then the single neural network model is trained with about 25 million images per epoch, even when only one class was newly added, and the rest of the data were trained in advance. Moreover, the depth of the neural network may also have to be significantly rescaled and recalibrated to avoid any possible underfitting problem when the number of classes becomes very high. Therefore, learning with a single neural network is inappropriate for our problem of dealing with many product types.  
In the case of the ENN, each distributed backbone model receives a learnable workload for a more feasible model fitting. Suppose one backbone model can comfortably learn up to 1000 classes of data. For 50,000 classes, we can have 500 backbone models trained independently on different devices. If 100 extra classes are introduced, we can designate one backbone model to learn from the newly trained data and let it pass the weighted input to the ensemble DNN model. The other backbone models pretrained on the previous 50,000 classes do not have to be retrained. The retraining at the ensemble DNN layer (the master layer) is performed quicker than the single neural network model as it only needs to account for the weighted input of the newly trained distributed backbone models. Therefore, the ENN model only incurs learning costs proportionally to the amount of the new data.