Sparse Neural Network Inference With Thresholded Weights And Outputs
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Solution Overview
Problem
Existing neural networks require significant computational resources and memory due to dense weight and layer output structures, which can be inefficient and prone to instability with noisy inputs.
Innovation Solution
Implementing a sparse neural network with sparsity thresholds for weights and layer outputs to improve computational efficiency and robustness, using sparse representations that maintain competitive accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense weight and layer output structures are used in neural networks, then prediction accuracy can be maintained, but computational resources and memory usage increase significantly
Solution Approach 1:
The patent changes the density parameter of weight matrices and layer outputs from dense (1.0) to sparse (0.0) representations. By applying sparsity thresholds to both weights and layer outputs, the system reduces computational resources while maintaining prediction accuracy through the sparse neural network architecture that processes only non-zero elements.
2Measurement precision
If dense weight and layer output structures are used in neural networks, then prediction accuracy can be maintained, but memory usage increases significantly
Solution Approach 1:
The patent changes the density parameter of weight matrices and layer outputs from dense (1.0) to sparse (0.0) representations. By applying sparsity thresholds to both weights and layer outputs, the system reduces memory usage while maintaining prediction accuracy through the sparse neural network architecture that stores and processes only non-zero elements.
3Productivity
If dense neural network structures are used, then comprehensive data processing can be achieved, but computational efficiency decreases
Solution Approach 1:
The patent changes the density parameter from dense to sparse representations for both weights and layer outputs. This parameter change reduces network structure complexity by eliminating zero-weight connections and zero-output nodes, thereby improving computational efficiency while maintaining essential data processing capabilities through only the non-zero elements.
Data Source
AI summary
An inference system trains and performs inference using a sparse neural network. The sparse neural network may include one or more layers, and each layer may be associated with a set of sparse weights that represent sparse connections between nodes of a layer and nodes of a previous layer. A layer output may be generated by applying the set of sparse weights associated with the layer to the layer output of a previous layer. Moreover, the one or more layers of the sparse neural network may generate sparse layer outputs. By using sparse representations of weights and layer outputs, robustness and stability of the neural network can be significantly improved, while maintaining competitive accuracy.