Neural network critical neuron selection
AssigneeEntanglement, Inc.
InventorAmit VERMA
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Abstract
The present disclosure relates to systems and methods for optimizing neural networks by strategically identifying and pruning critical neurons to reduce computational resources while maintaining high levels of accuracy. The method involves determining critical neurons within a neural network based on features collected during an initial phase of training. These critical neurons are then pruned from the network, resulting in a pruned neural network with the critical neurons removed. The training process continues using the pruned neural network, allowing for significant computational savings without substantially impacting the network's performance.
Claims20
A method comprising:
- determining critical neurons in a neural network;
- pruning the critical neurons from the neural network, wherein the pruning generates a pruned neural network with the critical neurons removed;
- and
- continuing a training process using the pruned neural network.
A critical neuron pruning system comprising:
- a memory;
- and
- a processor that is configured to execute machine readable instructions stored in the memory for causing the processor to:
- determine critical neurons in a neural network;
- prune the critical neurons from the neural network, wherein the pruning generates a pruned neural network with the critical neurons removed;
- and
- continue a training process using the pruned neural network.
A method, comprising:
- receiving a normalized feature vector corresponding to each neuron in a layer of a neural network;
- calculating a score for each neuron in the layer, wherein the score is based on the normalized feature vector and features collected during training of the neural network;
- and
- determining a set of neurons having a score lower than a threshold, wherein the neurons in the set of neurons are identified as critical neurons.
Dependencies are inferred from the claim text.