Efficient Recurrent Language Models with Joint Activity Sparsity and Connectivity Sparsity

Core Concepts

Jointly applying activity sparsity and connectivity sparsity in recurrent neural networks can significantly reduce computational requirements without proportional drop in task performance for language modeling.

Abstract

The authors investigate the joint effects of sparse activity and sparse connectivity in the Event-based Gated Recurrent Unit (EGRU), a biologically-inspired recurrent neural network architecture. They provide evidence that sparse activity and sparse connectivity are independent means of reducing computational operations, and that jointly applying both strategies only affects task performance for high degrees of sparsity beyond 80%.
The authors first compare the performance of densely activated LSTM and sparsely activated EGRU models on the Penn Treebank and WikiText-2 language modeling datasets. They show that the EGRU model, which combines sparse activations with sparse connectivity, achieves competitive results compared to dense LSTM baselines.
To understand the interaction between the two sparsities, the authors systematically prune the LSTM and EGRU models to varying degrees of connectivity sparsity. They find that the effects of sparse activations and sparse weights are multiplicative on the reduction in computational operations, as conjectured by prior work. The task performance degradation follows a similar trend for both models as connections are removed.
Furthermore, the authors uncover a mechanism that allows trading weight regularization for sparse activations in the EGRU model. They observe that the training process drives the mean values of weights and biases below 0, which promotes sparse network activity but interferes with standard weight decay regularization. This provides a way to tune the activity of the EGRU network to meet the requirements of a target hardware system.
Overall, the results suggest that sparsely connected event-based neural networks are promising candidates for efficient and effective sequence modeling on neuromorphic hardware.

Stats

The number of multiply-accumulate (MAC) operations for the recurrent model only (without token read-out) is reported for the LSTM and EGRU models at different levels of connectivity sparsity on the Penn Treebank and WikiText-2 datasets.

Quotes

"Jointly applying both strategies only affects task performance for high degrees of sparsity beyond 80%."
"The training process drives the mean values of weights and biases below 0, which promotes sparse network activity but interferes with standard weight decay regularization."

Key Insights Distilled From

Weight Sparsity Complements Activity Sparsity in Neuromorphic Language Models

by Rish... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00433.pdf

Deeper Inquiries

How can the insights from this work be extended to larger-scale language modeling tasks and more complex neural network architectures

The insights gained from this study on the synergies between activity sparsity and connectivity sparsity in event-based neural networks can be extended to larger-scale language modeling tasks and more complex neural network architectures by scaling up the experiments and exploring the effects on models with higher parameters and more complex structures. For larger-scale language modeling tasks, the trade-off between activity sparsity and connectivity sparsity can be further investigated to understand how these sparsities interact and impact performance as the model size increases. Additionally, exploring the application of these sparsity techniques in more complex neural network architectures, such as transformer-based models or hybrid architectures, can provide valuable insights into their effectiveness and efficiency in diverse settings.

What other techniques, beyond weight pruning, could be used to further improve the efficiency of event-based neural networks for language modeling

Beyond weight pruning, several other techniques can be employed to enhance the efficiency of event-based neural networks for language modeling. One approach is to incorporate quantization techniques to reduce the precision of weights and activations, leading to lower memory and computational requirements. Additionally, techniques like knowledge distillation can be utilized to transfer knowledge from a larger, more complex model to a smaller, more efficient event-based neural network. Furthermore, exploring sparsity-inducing regularizers, such as group lasso or elastic net, can encourage sparsity in both activations and weights, further improving efficiency without compromising performance. Techniques like dynamic network architectures, where the network structure evolves during training based on the task requirements, can also be explored to optimize efficiency in event-based neural networks for language modeling.

How can the trade-off between activity sparsity and weight regularization be more formally characterized and optimized for a given hardware platform and performance target

The trade-off between activity sparsity and weight regularization can be formally characterized and optimized for a given hardware platform and performance target by conducting systematic experiments to analyze the impact of varying levels of weight decay on network activity and task performance. By studying the relationship between weight decay rates, network sparsity, and task performance, a comprehensive understanding of how weight regularization influences activity sparsity can be obtained. Optimization techniques, such as grid search or Bayesian optimization, can be employed to find the optimal balance between weight decay and activity sparsity for a specific hardware platform. Additionally, developing mathematical models or algorithms that explicitly consider the trade-off between activity sparsity and weight regularization can help in optimizing neural network architectures for efficient inference on different hardware platforms.