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Understanding Model Transformation and Intermediate Representations Model transformation involves re-expressing a neural network's structure and parameters into a format suitable for execution in a target environment. A core aspect of this process is interpreting the model’s computation graph—its directed acyclic graph (DAG) of operations—and a ...

Modern deep-learning stacks rely on a computation graph to represent a neural network as a directed acyclic graph (DAG) whose nodes are tensor operations and whose edges carry multi-dimensional arrays (tensors). This abstraction allows the framework to reason about the entire model ahead of time, insert missing backward operations, schedule ker ...

Modern AI frameworks rely on computation graphs as the fundamental abstraction for representing and executing neural network models. By using universal data structures like tensors to interpret and perform neural network operations, computation graphs enable systematic analysis and optimization of AI systems. Motivation: Challenges in AI Engine ...