Track 2 (ML): Models similar to the Cerebras modelzoo models

ML models that are a combination of the building blocks used by modelzoo models and/or the layers supported by Cerebras as listed in their documentation.

Good Match Criteria: You would be a good match for this track if your research already uses, or could potentially use, the specific TensorFlow or PyTorch layers listed below for implementing the models.

Supported Core Compute Kernels

Cerebras TensorFlow Layer API

• Please check the list of supported Tensorflow layers here.

Cerebras PyTorch Layer API

• PyTorch Layer API: custom wrapper of several commonly used PyTorch APIs
• Supported PyTorch Operations
  • Please check the list of supported PyTorch layers and operations here.

Track Specific Questions

If your project falls under this category, make sure to address the following questions in your application document:

1. Please, indicate which building blocks listed in Tensorflow layers supported or PyTorch functionality supported you propose to use.
2. Please, attach a diagram that describes your proposed model, along the lines illustrated in this example:
3. How big is the model you intend to train on Neocortex? Please, describe in terms of the number of layers or kernels that comprise it. Include size in terms of MB as well.
4. How big is the dataset (number of samples, MB per sample) you intend to use?
5. Please specify the shapes of the input and output tensors for your model/s.
6. If possible, please specify the name of the dimensions for your input and output tensors from the previous question. E.g. (batch, input channels, height, width)
7. Please specify the loss function that you would like to use.
8. Please, list the libraries complementary to standard PyTorch and/or TensorFlow distributions that you would need to train your model(s).
9. Please, list the key libraries that you would need for data preprocessing.