Joint channel, layer, and block pruning for neural networks according to latency constraints
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Solution Overview
Problem
Conventional neural network pruning techniques fail to achieve significant parameter reduction (60%-90%) without significantly reducing accuracy, as they do not accurately model latency variations across multiple dimensions, leading to suboptimal trade-offs between accuracy and latency.
Innovation Solution
Implement joint channel, layer, and block pruning using mixed-integer nonlinear programming (MINLP) to optimize neural network structure, considering latency cost matrices and importance scores, allowing for precise latency estimation and maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional pruning techniques are used to reduce parameter count, then parameter reduction is achieved (30%-40%), but accuracy significantly drops
Solution Approach 1:
The patent segments the pruning process into three distinct dimensions: channel pruning (removing redundant feature channels), layer pruning (removing entire neural network layers), and block pruning (removing groups of consecutive layers). This multi-dimensional segmentation allows for more granular control over which parameters are removed, enabling aggressive parameter reduction while preserving accuracy-critical components. The segmentation is implemented through separate importance scoring mechanisms for channels, layers, and blocks, which are then jointly optimized to achieve the desired pruning ratio while maintaining model performance.
Solution Approach 2:
The patent extends conventional single-dimensional channel pruning into a three-dimensional pruning space by introducing layer and block dimensions. This dimensional expansion allows the optimization to operate simultaneously across channel, layer, and block axes, creating a more comprehensive view of parameter importance. The MINLP formulation captures interactions across all three dimensions, enabling the system to make coordinated pruning decisions that conventional single-dimensional methods cannot achieve, thus reducing parameters more effectively while preserving accuracy.
2Loss of time
If parameter count is reduced to achieve target latency (60%-90% reduction), then latency requirements are met, but accuracy significantly drops
Solution Approach 1:
The patent introduces dynamic importance scoring that adapts to different pruning ratios and target latency requirements. The importance scores for channels, layers, and blocks are computed based on their actual contribution to model accuracy, allowing the system to dynamically adjust which parameters are removed at different pruning stages. This dynamic approach enables the system to meet aggressive latency targets (60%-90% parameter reduction) while preserving accuracy by selectively removing only the least important parameters at each dimension.
Solution Approach 2:
The patent changes the optimization parameters from conventional single-criterion pruning to a multi-criterion MINLP formulation that simultaneously optimizes for parameter reduction ratio and accuracy preservation. The formulation includes constraints on minimum accuracy thresholds and objective functions that balance parameter reduction with performance maintenance. This parameter change enables the system to achieve target latency requirements through 60%-90% parameter reduction while maintaining acceptable accuracy levels, unlike conventional methods that sacrifice accuracy to achieve parameter reduction.
3Quantity of substance
If joint channel, layer, and block pruning is implemented, then parameter reduction is significantly improved (60%-90%), but computational complexity increases
Solution Approach 1:
The patent performs preliminary importance scoring for channels, layers, and blocks before the actual pruning decision is made. These preliminary scores are computed using efficient approximations that estimate parameter importance without requiring full model retraining or complex computations. The preliminary scoring results are then used as inputs to the MINLP optimization, which makes the final pruning decisions. This preliminary action reduces the computational burden of the joint pruning process by pre-processing importance information, enabling 60%-90% parameter reduction while managing computational complexity.
Solution Approach 2:
The patent introduces importance scores as intermediary variables that mediate between the complex joint pruning decisions and the final parameter removal. Instead of directly optimizing for parameter reduction across all three dimensions simultaneously, the system first computes importance scores for channels, layers, and blocks as intermediary representations of their contribution to model performance. These intermediary scores then guide the MINLP optimization, simplifying the computational complexity while enabling comprehensive joint pruning that achieves 60%-90% parameter reduction.
4Productivity
If MINLP optimization is used for joint pruning, then optimal pruned structure is determined in single forward pass, but optimization complexity increases
Solution Approach 1:
The patent merges the channel pruning, layer pruning, and block pruning decisions into a single unified MINLP optimization problem. Instead of performing separate pruning operations for each dimension, the formulation combines all three into one joint optimization that determines the optimal pruned structure across all dimensions simultaneously. This merging is achieved by defining binary decision variables for channels, layers, and blocks within a single MINLP framework, allowing the system to determine the optimal pruned structure in a single forward pass while capturing interactions between all pruning dimensions. The unified formulation improves pruning efficiency by eliminating the need for multiple sequential pruning passes, despite the increased optimization complexity.
Data Source
AI summary
In various examples, systems and methods are disclosed relating to jointly pruning channels, layers, and/or blocks of neural networks according to target latency constraints. One or more circuits can determine a plurality of importance scores for a plurality of layers of a neural network and can generate a latency cost data structure for the neural network. The one or more circuits can prune the neural network based at least on the plurality of importance scores, the latency cost data structure, and a target latency value.