Transformer Pruning and Quantization for Low-Energy Acceleration
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Solution Overview
Problem
Existing transformer accelerators face challenges in efficiently executing computation-intensive transformer models while minimizing energy consumption and optimizing computational performance for real-time applications.
Innovation Solution
A method involving transformer model optimization through pruning and quantization, including coarse-grained and fine-grained pruning, dynamic post-training quantization, and head scheduling to optimize the placement of transformer model heads, combined with tile-based dynamic fixed-point quantization to accelerate operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If transformer model operations are executed with full precision and without pruning, then computational accuracy is maintained, but computational efficiency decreases and energy consumption increases
Solution Approach 1:
The patent applies parameter changes by implementing dynamic post-training quantization that transforms model weights and activations from high precision (FP32) to lower precision (INT8) representations. This quantization process modifies the numerical parameters of the transformer model to enable faster computation with reduced energy consumption while maintaining acceptable accuracy through careful calibration and scaling factor optimization.
Solution Approach 2:
The patent extracts and removes redundant or less important components from the transformer model through pruning techniques. By identifying and eliminating unnecessary neurons, connections, or attention heads that contribute minimally to the overall model performance, the system reduces computational complexity and accelerates inference while preserving the essential functional capabilities of the model.
2Productivity
If pruning is applied to reduce model size and accelerate computation, then computational performance improves, but model accuracy deteriorates
Solution Approach 1:
The patent employs preliminary action by performing pruning operations during the training phase or before deployment. Importance scoring mechanisms are applied in advance to identify critical components that must be preserved, and pruning decisions are made beforehand based on these scores. This preliminary analysis ensures that only non-essential elements are removed, protecting the model's accuracy while achieving computational acceleration.
Solution Approach 2:
The patent implements feedback mechanisms through iterative pruning processes where model performance is continuously evaluated after each pruning step. Importance scores are recalculated based on the current model state, and pruning decisions are adjusted according to performance feedback. This closed-loop approach allows the system to maintain accuracy thresholds while progressively optimizing for computational efficiency.
3Loss of time
If coarse-grained pruning is performed first, then processing time is reduced, but fine-grained optimization opportunities are lost
Solution Approach 1:
The patent applies segmentation by dividing the pruning process into distinct hierarchical stages: coarse-grained pruning that operates on large structural units (such as entire attention heads or neuron groups), followed by fine-grained pruning that operates on individual connections or parameters. This segmented approach allows the system to quickly eliminate obviously redundant components in the coarse stage, then apply more sophisticated optimization techniques in the fine stage without excessive computational overhead.
Solution Approach 2:
The patent implements dynamics by making the pruning granularity adaptive based on the model's characteristics and performance requirements. The system dynamically adjusts the level of pruning detail applied at each stage, transitioning from broad coarse-grained operations to more targeted fine-grained operations. This dynamic adaptation allows the pruning process to balance computational efficiency with optimization thoroughness based on real-time performance feedback.
4Use of energy by moving object
If quantization is applied to reduce precision, then energy consumption decreases, but numerical precision is lost
Solution Approach 1:
The patent applies partial action by implementing quantization selectively on different parts of the transformer model rather than uniformly across all operations. Critical computational paths that require high precision (such as attention score calculations) may maintain higher precision, while less sensitive operations (such as feed-forward network activations) are quantized to lower precision. This selective quantization approach reduces overall energy consumption while preserving numerical precision where it is most needed.
Data Source
AI summary
Disclosed are a transformer model optimization and head scheduling method and a transformer acceleration method, which may include: receiving dense scheduling data and a zero-line mask generated using the transformer model optimization and head scheduling method; outputting a dense operation result by performing a tiled matrix multiplication on the received dense scheduling data; and outputting a final operation result by transforming the dense operation result into a sparse matrix, using the zero-line mask.