Neural Network Sparsification Target Layer Determination
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Solution Overview
Problem
Existing methods struggle to fully utilize sparsity in neural network weights for speedup, as constraints from batch size, number of channels, and hardware operations limit sparsity utilization, leading to inconsistent and sometimes negligible speed improvements.
Innovation Solution
A sparsification target layer determination apparatus and method that investigates the execution time contribution of each layer in a neural network, comparing dense and sparse weight models to determine whether sparsification should be applied to each layer, thereby optimizing speedup.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If sparsification is applied to neural network weights, then execution speed is improved, but model accuracy is degraded
Solution Approach 1:
The patent applies different sparsification strategies to different layers of the neural network based on their specific characteristics. By analyzing each layer's contribution to execution time and accuracy, the system selectively sparsifies only those layers where it provides the most benefit, rather than uniformly applying sparsification across all layers. This local optimization resolves the contradiction by maintaining accuracy in critical layers while achieving speedup in computationally intensive layers.
Solution Approach 2:
The patent dynamically adjusts the sparsification level (degree of sparsity) as a parameter based on layer-specific analysis. By changing the sparsification parameter from a fixed value to a layer-dependent variable, the system optimizes the balance between speed and accuracy for each layer individually, allowing higher sparsification in layers where it benefits performance more and lower sparsification where it might harm accuracy.
2Speed
If higher degree of sparsity is applied, then execution speed is speeded-up more, but constraints from batch size, number of channels, and hardware operations limit further optimization
Solution Approach 1:
The patent segments the neural network into individual layers and analyzes each layer's execution time contribution separately. By breaking down the overall computation into discrete layer-level operations, the system can identify which layers are most suitable for sparsification based on their specific computational characteristics and hardware constraints, rather than treating the entire network as a monolithic unit.
Solution Approach 2:
The patent applies partial sparsification rather than complete sparsification to each layer. By using partial action (applying sparsification only to the extent beneficial for each layer), the system achieves meaningful speedup while avoiding the excessive action that would trigger hardware constraints and operational complexities.
3Ease of manufacture
If sparsification is applied to all layers uniformly, then implementation is simple, but execution speedup is suboptimal due to varying layer characteristics
Solution Approach 1:
The patent introduces dynamic adaptability to the sparsification process by automatically analyzing each layer's characteristics and adjusting the sparsification strategy accordingly. Rather than using a static uniform approach, the system dynamically determines the optimal sparsification level for each layer based on its computational profile, achieving both ease of implementation through automation and optimal speedup through adaptation.
Data Source
AI summary
A sparsification target layer determination apparatus includes: an each-layer sparsity speed contribution investigation part which receives a neural network model which includes a plurality of layers each of which has weights and one or more sparse weight neural network models which have sparse weights obtained by applying sparsification to the weights, layer by layer, and investigates, layer by layer, an execution time of the neural network model and one or more execution times of the one or more sparse weight neural network models; and a sparsification target layer determination part which determines whether or not to apply sparsification to the weights of the neural network model, layer by layer, based on a result of the investigation.