Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum

Abstract: A variety of pruning methods have been introduced for over-parameterized Recurrent Neural Networks to improve efficiency in terms of power consumption and storage utilization. These advances motivate a new paradigm, termed `hyperpruning', which seeks to identify the most suitable pruning strategy for a given network architecture and application. Unlike conventional hyperparameter search, where the optimal configuration's accuracy remains uncertain, in the context of network pruning, the accuracy of the dense model sets the target for the accuracy of the pruned one. The goal, therefore, is to discover pruned variants that match or even surpass this established accuracy. However, exhaustive search over pruning configurations is computationally expensive and lacks early performance guarantees. To address this challenge, we propose a novel Lyapunov Spectrum (LS)-based distance metric that enables early comparison between pruned and dense networks, allowing accurate prediction of post-training performance. By integrating this LS-based distance with standard hyperparameter optimization algorithms, we introduce an efficient hyperpruning framework, termed LS-based Hyperpruning (LSH). LSH reduces search time by an order of magnitude compared to conventional approaches relying on full training. Experiments on stacked LSTM and RHN architectures using the Penn Treebank dataset, and on AWD-LSTM-MoS using WikiText-2, demonstrate that under fixed training budgets and target pruning ratios, LSH consistently identifies superior pruned models. Remarkably, these pruned variants not only outperform those selected by loss-based baseline but also exceed the performance of their dense counterpart.

• The paper introduces hyperpruning, which uses a Lyapunov Spectrum-based distance metric to estimate pruned RNN performance against dense models.
• It applies dynamic flow analysis of network states to optimize hyperparameter searches, reducing computational costs while maintaining target accuracy.
• Empirical tests on LSTM and RHN architectures demonstrate that hyperpruned models consistently outperform traditional pruning methods on language tasks.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum

The paper introduces an innovative approach to network pruning, emphasizing the search for optimal pruned configurations of Recurrent Neural Networks (RNNs) driven by the Lyapunov Spectrum (LS) metric. This paradigm, termed "hyperpruning," refines the conventional network pruning techniques and aims to identify pruned network variants that not only meet but can potentially exceed the accuracy of their dense counterparts.

Methodology

Hyperpruning diverges from traditional hyperparameter searching that often lacks accuracy guarantees. Instead, it establishes that the accuracy of the dense model acts as a target benchmark for the pruned variants. The primary objective is to discover configurations that match or eclipse this accuracy, leveraging a novel LS-based distance metric. This distance metric provides an early performance indication by comparing the dynamic flow characteristics of pruned and dense networks. Specifically, the LS captures the contraction and expansion tendencies of network states, rendering it a reliable estimator of network trainability and performance potential. By embedding this LS-based distance within hyperparameter optimization algorithms, the LS-based Hyperpruning (LSH) framework significantly reduces the computational demand associated with exhaustive search.

Experimental Results

The paper provides robust empirical validation through extensive experiments employing LSTM and RHN architectures on language modeling benchmarks such as the Penn Treebank dataset and AWD-LSTM-MoS on WikiText-2. Under predefined training budgets and target pruning ratios, LSH consistently selects superior pruned models. Notably, these pruned models outperform traditional loss-based selection methods and their dense counterparts, demonstrating improved accuracy even when high sparsity ratios are targeted.

Implications and Future Directions

The implications of this work extend to both theoretical and practical spheres in the field of AI, particularly in optimizing neural network architectures for resource-constrained environments without sacrificing accuracy. The LS-based metric offers a principled approach to predict network behavior early in the training process, promoting efficiency and effectiveness in model deployment. This aligns well with real-world applications where computational overhead and storage limitations are critical concerns.

Future work could explore expanding the LS-based metric to broader types of neural networks beyond RNNs, exploring its applicability in CNNs and transformers. Furthermore, integrating this framework with more advanced hyperparameter optimization algorithms could potentially further reduce search resources and enhance system performance. The research opens a promising pathway to refine automated network pruning, making it more adaptive and predictive in its approach.

In conclusion, this paper contributes a meaningful stride in enhancing network pruning methodologies via Lyapunov Spectrum-driven metrics, fostering the development of efficient, scalable, and highly accurate neural networks suitable for expansive AI applications.