Neural Network Kernel Channel Ordering for Computational Efficiency
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Solution Overview
Problem
Convolutional neural networks require a large number of kernel computations, leading to significant processing power consumption and memory fetches, as each layer processes multiple kernels to identify and classify input data, often unnecessarily processing kernels that do not contribute to the final classification.
Innovation Solution
A method and system that order kernel channels based on their potential contribution, convolve the most impactful channels first, and determine whether to process subsequent channels by considering their potential contribution in combination with the convolution output, thereby reducing unnecessary computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all kernel channels are processed to ensure comprehensive feature extraction, then classification accuracy is improved, but processing time and computational resources are increased
Solution Approach 1:
The patent applies preliminary action by ordering kernel channels based on their potential contribution to classification before processing. The system pre-ranks channels using metrics such as L1 norm of weights or activation statistics from previous layers, so that when processing, the most impactful channels are handled first. This allows the system to prepare the processing sequence in advance without requiring actual classification to determine channel importance.
Solution Approach 2:
The patent implements partial action by selectively processing only the top-k most important kernel channels rather than all channels. The system determines a threshold or selects top-k channels based on their ordered importance metrics, processes only those, and uses the results for classification. This partial processing approach reduces computational time while maintaining sufficient accuracy by focusing on the most contributing channels.
2Reliability
If all kernel channels are processed to maintain comprehensive feature representation, then model reliability is improved, but energy consumption increases
Solution Approach 1:
The system performs preliminary ordering of kernel channels based on their potential contribution metrics before energy-intensive convolution operations. By pre-ranking channels using lightweight criteria such as weight magnitude (L1 norm) or activation patterns from prior layers, the system prepares an optimized processing sequence that minimizes energy consumption while maintaining model reliability through focused processing of top channels.
Solution Approach 2:
The patent applies partial action by processing only the essential top-k kernel channels rather than all channels, thereby reducing energy consumption. The system determines the optimal number of channels to process based on their ordered importance, allowing the model to achieve sufficient reliability with reduced computational workload, thus lowering energy consumption without sacrificing critical classification performance.
3Ease of manufacture
If kernel channels are processed in standard order, then implementation simplicity is maintained, but computational efficiency is reduced
Solution Approach 1:
The patent applies preliminary action by pre-ordering kernel channels based on their potential contribution before the main processing loop. The system calculates ordering metrics such as L1 norm of weights or activation statistics in advance and stores the ordered sequence. During execution, the system simply follows the pre-computed order, which maintains implementation simplicity while significantly improving computational efficiency by processing the most important channels first.
Solution Approach 2:
The patent implements dynamics by making the processing sequence adaptive rather than fixed. Instead of always processing channels in a predetermined order, the system dynamically orders channels based on their measured contribution metrics for each specific input or task. This dynamic ordering allows the system to optimize computational efficiency for each case while maintaining a relatively simple implementation through automated metric calculation and sorting.
Data Source
AI summary
A method of reducing kernel computations; the method comprising ordering a plurality of kernel channels. A first of the ordered kernel channels is then convolved with input data to produce a convolution output, and it is determined whether to convolve one or more subsequent kernel channels of the ordered kernel channels. Determining whether to convolve subsequent kernel channels comprises considering a potential contribution of at least one of the one or more subsequent kernel channels in combination with the convolution output.