Deep Neural Net (DNN) Model Inference Task

A Deep Neural Net (DNN) Model Inference Task is a neural network model inference task (to make predictions or decisions based on new, unseen data) for deep NNets.

• Context:
  • It can (typically) involve processing input data through multiple layers of the neural network.
  • It can (typically) be executed on various hardware platforms, ranging from high-performance GPUs and TPUs.
  • It can (often) require optimization techniques such as quantization technique, pruning technique, and compression technique to improve inference speed and reduce memory footprint.
  • It can (typically) be supported by a DNN Model Inference System (that implements a DNN model inference algorithm).
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• Example(s):
  • ...
• Counter-Example(s):
  • DNN Training, where the network learns from a dataset by adjusting its weights through backpropagation based on a loss function.
  • Traditional machine learning inference methods, such as: Decision Tree Inference and Logistic Regression Inference.
• See: Model Optimization, Hardware Acceleration, Real-Time Inference, AI Application Domains, Groq.

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