Structured Block Pruning for Automatic Speech Recognition
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Solution Overview
Problem
Existing automatic speech recognition (ASR) models, particularly conformer models, require significant computational resources and power to process audio data, posing a challenge for efficient deployment on hardware devices like GPUs.
Innovation Solution
Implementing block-wise pruning methods to reduce the complexity and computational requirements of neural network weight matrices by dividing them into blocks and selectively setting weights to zero based on heuristic constraints, optimizing for hardware acceleration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conformer models are used to achieve state-of-the-art ASR performance, then recognition accuracy is improved, but computational resource requirements and power consumption increase significantly
Solution Approach 1:
The weight matrix is divided into multiple blocks, allowing selective pruning of entire blocks based on their contribution to model performance. This segmentation enables the model to retain only the most important computational paths while removing redundant blocks, thereby reducing power consumption while maintaining recognition accuracy.
Solution Approach 2:
The method extracts and identifies critical blocks within the weight matrix that contribute most to ASR performance. By selectively removing non-critical blocks through pruning, the model achieves reduced computational requirements and lower power consumption while preserving the essential functionality needed for accurate speech recognition.
2Loss of information
If conformer models process every audio corpus to achieve comprehensive feature extraction, then feature completeness is improved, but computational complexity and processing time increase
Solution Approach 1:
By segmenting the weight matrix into blocks, the method enables selective processing where only essential blocks are retained and processed. This segmentation allows the model to maintain access to critical feature extraction capabilities while avoiding the computational burden of processing all possible audio corpus features, thereby reducing computational complexity.
Solution Approach 2:
The pruning method applies partial action by retaining only the necessary portions of the weight matrix that are essential for effective feature extraction. Instead of processing every possible feature transformation, the model performs partial computations on selected blocks, reducing computational complexity while still achieving sufficient feature completeness for accurate ASR.
3Measurement precision
If full weight matrices are maintained to preserve model accuracy, then recognition performance is improved, but memory requirements and computational overhead increase
Solution Approach 1:
The weight matrix is segmented into multiple blocks, allowing the system to store and process only the essential blocks rather than the complete weight matrix. This segmentation reduces memory requirements by eliminating redundant weight storage while preserving the structural integrity needed for maintaining recognition performance.
Solution Approach 2:
Different blocks within the weight matrix are treated differently based on their importance. Critical blocks are retained with full precision to maintain recognition performance, while non-critical blocks are pruned or compressed. This local quality approach ensures that memory resources are allocated efficiently, preserving performance where needed while reducing overall memory requirements.
Data Source
AI summary
Methods and apparatuses for automatic speech recognition are provided. The method includes: generating a weight matrix for a layer of a plurality of layers in a neural network; dividing the weight matrix into a plurality of blocks, each block including a plurality of weights; selecting a set of blocks from the plurality of blocks for block-wise pruning by minimizing a cost function subject to a pre-determined block-wise constraint; and generating a block-wise pruned weight matrix by setting one or more weights in the set of blocks to zero. The weight matrix includes a set of weights associated with the layer, the plurality of layers includes a first layer receiving a first input associated with one or more audio feature sequences, and the plurality of layers are executed on one or more processors.