Vision Transformer Pruning for Memory and Throughput Bottlenecks
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Solution Overview
Problem
Modern deep vision transformers (ViTs) face challenges in memory usage and throughput, particularly in time-sensitive, low-power settings such as pedestrian and vehicle detection in advanced driver assistance systems, due to unnecessary portions of blocks that slow down performance and increase memory usage.
Innovation Solution
Pruning vision transformers by analyzing parameters and removing those with scores below a threshold, resulting in a compressed model that reduces memory usage and increases throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vision transformer blocks are designed with comprehensive parameters to handle diverse vision processing tasks, then task coverage and accuracy are improved, but memory usage increases and throughput decreases
Solution Approach 1:
The patent extracts and removes unnecessary parameters from vision transformer blocks based on importance scoring. By analyzing which parameters contribute most to task performance and removing those with low importance scores, the system reduces memory usage while preserving essential functionality for diverse vision tasks.
Solution Approach 2:
The patent applies different pruning strategies to different parameters within blocks based on their individual importance scores. Rather than uniformly reducing all parameters, the system selectively removes only those with low importance, maintaining high-quality performance for critical parameters while reducing complexity for less important ones.
2Measurement precision
If vision transformer blocks include all necessary parameters for accurate processing, then processing accuracy is improved, but throughput decreases due to increased computational overhead
Solution Approach 1:
The patent removes computationally expensive parameters that contribute minimally to processing accuracy. By eliminating low-importance parameters through scoring and threshold-based removal, the system reduces computational overhead and increases throughput while maintaining necessary accuracy for vision tasks.
Solution Approach 2:
The patent dynamically adjusts parameter retention based on importance scores calculated from training data. Parameters above a certain importance threshold are retained to maintain accuracy, while those below the threshold are removed to improve throughput, creating an optimized balance between precision and productivity.
3Reliability
If comprehensive parameters are used in vision transformer blocks, then performance on diverse tasks is improved, but frames per second decreases due to slower inference
Solution Approach 1:
The patent extracts and eliminates parameters that slow down inference without contributing significantly to task performance. By removing low-importance parameters through importance scoring, the system reduces computational burden and increases frames per second while preserving performance on diverse vision tasks.
Solution Approach 2:
The patent applies selective parameter retention where critical parameters for task performance are preserved while non-critical parameters are removed. This localized optimization maintains high task performance while significantly improving inference speed and frames per second.
4Use of energy by moving object
If vision transformer deployment targets low-power settings, then energy efficiency is improved, but requires reduction in model size and complexity
Solution Approach 1:
The patent removes unnecessary parameters to reduce overall model size, making the vision transformer suitable for deployment in low-power settings. By eliminating low-importance parameters through scoring mechanisms, the system achieves compact model size while retaining essential functionality.
Solution Approach 2:
The patent optimizes parameter selection based on importance metrics to create a compact model configuration. By retaining only high-importance parameters and removing others, the system achieves energy-efficient operation in low-power settings while maintaining acceptable performance.
Data Source
AI summary
A vision transformer is a deep learning model used to perform vision processing tasks such as image recognition. Vision transformers are currently designed with a plurality of same-size blocks that perform the vision processing tasks. However, some portions of these blocks are unnecessary and not only slow down the vision transformer but use more memory than required. In response, parameters of these blocks are analyzed to determine a score for each parameter, and if the score falls below a threshold, the parameter is removed from the associated block. This reduces a size of the resulting vision transformer, which reduces unnecessary memory usage and increases performance.