Block-Sparse RNNs via Weight Pruning and Group Lasso
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Solution Overview
Problem
Recurrent Neural Networks (RNNs) face inefficiencies in compute and memory usage due to unstructured sparsity, which does not effectively utilize hardware resources, leading to lower speed-ups on various hardware platforms.
Innovation Solution
Implementing block sparsity by pruning blocks of weights in RNNs during training, combined with group lasso regularization, to create block-sparse RNNs that reduce storage overhead and enhance hardware efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If unstructured sparsity is used in RNNs, then compute and memory requirements are reduced, but hardware resource utilization efficiency deteriorates
Solution Approach 1:
The patent divides the weight matrix into discrete blocks (e.g., 4x4 or 8x8 blocks) and applies sparsity at the block level rather than individual element level. This segmentation allows the system to maintain reduced compute and memory requirements while creating regular patterns that can be efficiently processed by hardware array data-paths, thus resolving the contradiction between resource reduction and hardware utilization efficiency.
2Quantity of substance
If sparse operations are used, then storage overhead is reduced, but speed-up is less than expected due to irregular memory access
Solution Approach 1:
The patent changes the sparsity parameter from unstructured (irregular) to structured (regular block patterns). This parameter change maintains storage overhead reduction while creating predictable, regular memory access patterns that can be efficiently cached and processed, thereby achieving both low storage overhead and high execution speed through improved memory access regularity.
3Volume of stationary object
If block sparsity is implemented, then model size is reduced, but accuracy may be affected
Solution Approach 1:
The patent applies block sparsity pruning gradually and selectively during training, removing only the least important blocks while retaining sufficient capacity to maintain model performance. This partial action approach achieves significant model size reduction (up to 10x) while carefully controlling accuracy degradation to acceptable levels (9-17% loss), balancing compression ratio with model reliability.
Data Source
AI summary
Described herein are systems and methods to prune deep neural network models in reducing the overall memory and compute requirements of these models. It is demonstrated that using block pruning and group lasso combined with pruning during training, block-sparse recurrent neural networks (RNNs) may be built as accurate as dense baseline models. Two different approaches are disclosed to induce block sparsity in neural network models: pruning blocks of weights in a layer and using group lasso regularization to create blocks of weights with zeros. Using these techniques, it is demonstrated that block-sparse RNNs with high sparsity can be created with small loss in accuracy. Block-sparse RNNs eliminate overheads related to data storage and irregular memory accesses while increasing hardware efficiency compared to unstructured sparsity.