Computational Impact of Activation Functions in Wide FFNs
A neural network designer is working on a large language model and decides to significantly increase the hidden layer dimension () of the Feed-Forward Network (FFN) sub-layers, making it much larger than the input dimension (). Explain why this design choice makes the computational efficiency of the non-linear activation function a more critical consideration than it would be in a network with a smaller hidden layer dimension.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
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An engineer is designing a neural network for a large language model and observes that the two-layer Feed-Forward Network (FFN) component is the primary computational bottleneck during training. The design specifies that the FFN's internal hidden layer dimension must be significantly larger than its input and output dimensions to ensure high model capacity. Given the goal of reducing the computational cost of the FFN while preserving its expressive power, which of the following design choices for the non-linear activation function (applied after the first linear layer) would be most effective?
Evaluating FFN Design Trade-offs in a Resource-Constrained LLM Project
Analyzing the Impact of FFN Width on Activation Function Choice
Computational Impact of Activation Functions in Wide FFNs