Natural Language Processing (NLP) is a field that deals with how computers understand and interpret human language. Recent advancements in NLP have led to the development of pre-trained language models, such as BERT and RoBERTa, which have significantly improved various downstream applications. However, these models are computationally expensive and require significant resources to deploy on edge devices. To address this challenge, researchers have proposed skimming acceleration schemes, which can reduce the computational complexity of these models without compromising their performance.
Skimming Acceleration Schemes
Skimming acceleration schemes aim to efficiently process language by selectively dropping unimportant tokens during inference. These schemes can be categorized into static and dynamic ones based on whether the remaining token ratio is fixed or not. In static skimming, the number of unimportant tokens to be dropped is fixed, while in dynamic skimming, the number of dropped tokens varies adaptively.
Black-box Scenario
In the black-box scenario, no internal information about the language model is available, making it challenging to optimize the skimming strategy. However, the goal of skimming-based language models remains the same: to reduce computational complexity by dropping unimportant tokens during inference. To overcome this challenge, researchers use character-level candidate sets to search for the best candidate that maximizes computation complexity increase.
Advantages and Challenges
Skimming acceleration schemes offer several advantages, including reduced computational complexity, faster processing times, and improved energy efficiency. However, implementing these schemes in edge devices poses significant challenges due to limited resources, such as memory and computational power. Additionally, the accuracy of skimming-based language models can be affected by factors like the choice of skimming scheme and the complexity of the language model.
Conclusion
Skimming acceleration schemes offer a promising solution for improving the efficiency of natural language processing models in edge devices. By selectively dropping unimportant tokens during inference, these schemes can reduce computational complexity without compromising performance. While implementing these schemes poses challenges, ongoing research seeks to overcome these challenges and further improve the efficiency of skimming-based language models.
