Q-S5: Towards Quantized State Space Models

Steven Abreu, Jens E. Pedersen, Kade M. Heckel, Alessandro Pierro·June 13, 2024

Summary

This paper investigates the impact of quantization on State Space Models (SSMs), particularly the S5 model, for computational efficiency in sequence modeling. The authors employ quantization-aware training (QAT) and post-training quantization (PTQ) on tasks like dynamical systems, Sequential MNIST, and Long Range Arena (LRA). They find that S5 models can maintain high accuracy (within 1% of full precision) for sMNIST and most LRA tasks, with recurrent weights being more sensitive to lower bit precisions. PTQ is effective for language tasks but not as much for others, emphasizing the need for QAT. The study highlights the trade-offs between quantization, performance, and hardware deployment, particularly in resource-constrained environments, and suggests future directions for optimizing SSMs and exploring binary activations for spatio-temporal signal coding.

Key findings

3

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper "Q-S5: Towards Quantized State Space Models" aims to address the challenge of developing fully quantized State Space Models (SSMs) based on the S5 architecture . This research focuses on creating quantized models that achieve high accuracy comparable to full-precision models while utilizing significantly less memory and predominantly integer operations . The goal is to explore the effectiveness of Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT) for language-based tasks, such as Text and Retrieval, within the context of Large Random Access (LRA) tasks . This problem of optimizing quantized models for efficient performance is not entirely new, but the specific approach and focus on SSMs and the S5 architecture represent a novel contribution to the field of neural network quantization .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the impact of quantization on State Space Models (SSMs) and its effect on model performance, particularly focusing on the S5 model. The study investigates how quantization, through quantization-aware training (QAT) and post-training quantization (PTQ), influences the performance of SSMs across various tasks such as dynamical systems modeling, Sequential MNIST (sMNIST), and Long Range Arena (LRA) tasks. The research systematically evaluates the sensitivity of SSMs to quantization, highlighting the degradation in performance for recurrent weights below 8-bit precision and the varying impact on different components of the models .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Towards Quantized State Space Models" proposes several new ideas, methods, and models in the field of sequence modeling architectures based on State Space Models (SSMs) . Here are some key points from the paper:

  1. Quantization Sensitivity Evaluation: The paper systematically evaluates the quantization sensitivity of SSMs using quantization-aware training (QAT) and post-training quantization (PTQ) across different tasks such as dynamical systems modeling, Sequential MNIST (sMNIST), and Long Range Arena (LRA) tasks . The study shows that performance on most tasks degrades significantly for recurrent weights below 8-bit precision, while other components can be compressed further without significant loss of performance.

  2. Fully Quantized S5 Models: The paper presents fully quantized S5 models that maintain test accuracy within a 1% drop on sMNIST and most of the LRA tasks . This indicates the effectiveness of quantization in reducing memory usage and performing integer operations efficiently while maintaining model accuracy.

  3. Quantization Techniques: The study highlights that post-training quantization (PTQ) performs well on language-based LRA tasks, whereas other tasks require quantization-aware training (QAT) . This distinction in the performance of different quantization techniques based on the task type provides insights for optimizing model efficiency.

  4. Future Directions: The paper outlines future directions for research, including exploring optimal tradeoffs between training compute and final model efficiency through quantization-aware finetuning (QAFT) methods . Additionally, the authors aim to extend their analysis to demonstrate how sparse binary activations can code complex patterns in spatio-temporal signals using recurrent linear maps, similar to the approach in SSMs .

Overall, the paper introduces innovative approaches to quantization in SSMs, evaluates the impact of quantization on model performance across various tasks, and suggests future research directions to enhance the efficiency and effectiveness of quantized state space models . The paper "Towards Quantized State Space Models" introduces several characteristics and advantages of the S5 model compared to previous methods, highlighting its efficiency and suitability for dynamical systems modeling. Here are the key points based on the details in the paper:

  1. Computational Efficiency: The S5 model, based on State Space Models (SSMs), offers computational efficiency compared to transformer architectures, particularly in terms of scalability with sequence length and performance on various sequence modeling tasks . This efficiency makes SSMs like the S5 model a potent alternative to transformers for next-generation sequence modeling architectures.

  2. Quantization Sensitivity: The study systematically evaluates the quantization sensitivity of SSMs using quantization-aware training (QAT) and post-training quantization (PTQ) across different tasks such as dynamical systems modeling, Sequential MNIST (sMNIST), and Long Range Arena (LRA) tasks . The results show that while performance degrades significantly for recurrent weights below 8-bit precision, other components can be compressed further without significant loss of performance.

  3. Model Performance: Fully quantized S5 models are presented in the paper, maintaining test accuracy within a 1% drop on sMNIST and most of the LRA tasks . This indicates that the S5 model can achieve high performance even with quantization, showcasing its robustness and effectiveness in maintaining accuracy while using significantly less memory and integer operations.

  4. Quantization Techniques: The study highlights the importance of quantization-aware training (QAT) for most tasks, with post-training quantization (PTQ) performing well specifically on language-based LRA tasks . This distinction in the performance of different quantization techniques provides insights into optimizing model efficiency based on the task requirements.

  5. Future Research Directions: The paper outlines future research directions, aiming to explore optimal tradeoffs between training compute and final model efficiency through quantization-aware finetuning (QAFT) methods . Additionally, the authors plan to extend their analysis to demonstrate how sparse binary activations can code complex patterns in spatio-temporal signals using recurrent linear maps, similar to the approach in SSMs, indicating a promising direction for further advancements in the field .

Overall, the S5 model based on State Space Models offers computational efficiency, robust performance under quantization constraints, and potential for further advancements in sequence modeling architectures .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of quantized state space models. Noteworthy researchers in this area include:

  • AmirAli Abdolrashidi, Lisa Wang, Shivani Agrawal, Jonathan Malmaud, Oleg Rybakov, Chas Leichner, and Lukasz Lew .
  • Joachim Ott, Zhouhan Lin, Ying Zhang, Shih-Chii Liu, and Yoshua Bengio .
  • Jens Egholm Pedersen, J¨org Conradt, and Tony Lindeberg .
  • Sumit Bam Shrestha, Jonathan Timcheck, Paxon Frady, Leobardo Campos-Macias, and Mike Davies .
  • Yehui Tang, Yunhe Wang, Jianyuan Guo, Zhijun Tu, Kai Han, Hailin Hu, and Dacheng Tao .

The key to the solution mentioned in the paper involves conducting experiments using quantization-aware training (QAT) and post-training quantization (PTQ). In QAT, the model is trained with dynamic quantization, while in PTQ, a full-precision model is quantized without training .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of various quantization configurations on tasks such as sMNIST and LRA tasks. The experiments involved testing different quantization setups, including PTQ and QAT, with variations in precision levels for weights and activations . Additionally, the paper included ablation studies to assess the impact of quantized operators like the quantized GELU activation function, hard sigmoid, and quantized layer norm operation on model performance . The results of the experiments were presented in tables showing test accuracies for different models and configurations .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided context . Regarding the open-source status of the code used in the research, the information about the code being open source is not provided in the context as well.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that need to be verified. The study systematically evaluates the impact of quantization on State Space Models (SSMs) across various tasks like dynamical systems modeling, Sequential MNIST (sMNIST), and Long Range Arena (LRA) tasks . The results show that the performance of the fully quantized S5 models only drops by less than 1% on sMNIST and most LRA tasks, indicating the effectiveness of quantization in maintaining model accuracy .

Furthermore, the study highlights that the performance degradation is significant for recurrent weights below 8-bit precision, while other components can be compressed further without a notable loss of performance . This finding supports the hypothesis that the precision of quantization plays a crucial role in the performance of SSMs. Additionally, the results demonstrate that post-training quantization (PTQ) performs well on language-based LRA tasks, whereas quantization-aware training (QAT) is required for other tasks, providing valuable insights into the optimal quantization methods for different types of tasks .

Overall, the experiments and results in the paper offer strong empirical evidence to support the scientific hypotheses related to the impact of quantization on State Space Models, shedding light on the quantization sensitivity of SSMs across various tasks and providing essential insights for the development of efficient and hardware-optimized SSMs .


What are the contributions of this paper?

The paper "Q-S5: Towards Quantized State Space Models" makes several contributions:

  • It explores efficient modeling of long sequences with structured state spaces .
  • The paper discusses the parameterization and initialization of diagonal state space models .
  • It presents research on methods and theories of quantized neural networks .
  • The study highlights the effectiveness of diagonal state spaces compared to structured state spaces .
  • It delves into the topic of quantization methods for efficient neural network inference .
  • The paper introduces the concept of linear-time sequence modeling with selective state spaces .
  • It provides insights into recurrent memory with optimal polynomial projections .
  • The research presents results from experiments on tasks like sMNIST and LRA, showcasing test accuracies for various models and techniques .
  • The study compares the performance of different quantization methods such as PTQ, QAT, and QAFT, showing that QAT outperforms PTQ and QAFT in certain scenarios .

What work can be continued in depth?

To further advance the research in quantized state space models, several areas of work can be continued in depth based on the provided document :

  • Optimal Tradeoffs in Training Compute and Model Efficiency: Future research can explore optimal tradeoffs between training compute and final model efficiency to enhance the performance of quantized state space models.
  • Quantization-Aware Finetuning (QAFT): Expanding QAFT methods can lead to improved model efficiency and performance, especially for language-based tasks like Text and Retrieval.
  • Exploration of Large Pre-Trained Selective SSMs: There is a potential to extend QAFT methods to large pre-trained selective state space models such as Mamba, Jamba, and Griffin, to further enhance their efficiency and effectiveness.
  • Sparse Binary Activations for Complex Patterns: The analysis can be extended to demonstrate how sparse binary activations (spikes) can encode complex patterns in spatio-temporal signals using linear kernels, similar to the approach in state space models.

By delving deeper into these areas, researchers can advance the understanding and application of quantized state space models for various tasks and domains.


Introduction
Background
Overview of State Space Models (SSMs) and the S5 model
Importance of computational efficiency in sequence modeling
Objective
To examine the effects of quantization on S5 models
Investigate quantization-aware training (QAT) and post-training quantization (PTQ)
Identify performance trade-offs and hardware implications
Methodology
Data Collection
Selection of tasks: dynamical systems, Sequential MNIST (sMNIST), and Long Range Arena (LRA)
Benchmark datasets and evaluation criteria
Data Preprocessing and Quantization Techniques
Quantization-Aware Training (QAT)
Implementation of QAT on S5 models
Comparison of full precision vs. quantized models for sMNIST and LRA tasks
Post-Training Quantization (PTQ)
Application of PTQ on S5 models
Evaluation of PTQ performance across different tasks, focusing on language vs. other tasks
Sensitivity Analysis
Recurrent weight quantization sensitivity analysis
Identification of the most affected components in the model
Results and Discussion
Accuracy and Performance
Quantized model accuracy (within 1% of full precision) for sMNIST and LRA tasks
Comparison of QAT and PTQ results
Hardware Efficiency and Deployment
Trade-offs between quantization, computational cost, and memory footprint
Resource-constrained environment implications
Case Studies
Real-world scenarios and hardware deployment considerations
Future Directions
Suggestions for optimizing SSMs, including binary activations
Exploration of spatio-temporal signal coding with binary activations
Conclusion
Summary of key findings and implications for the state space modeling community
Limitations and potential future research directions in quantized SSMs
Basic info
papers
neural and evolutionary computing
machine learning
artificial intelligence
Advanced features
Insights
How do the authors test the impact of quantization on S5 models?
Which type of quantization is more effective for language tasks according to the study?
What method does the paper use to improve the efficiency of State Space Models for sequence modeling?
What is the accuracy drop observed for S5 models on sMNIST and most LRA tasks after quantization?

Q-S5: Towards Quantized State Space Models

Steven Abreu, Jens E. Pedersen, Kade M. Heckel, Alessandro Pierro·June 13, 2024

Summary

This paper investigates the impact of quantization on State Space Models (SSMs), particularly the S5 model, for computational efficiency in sequence modeling. The authors employ quantization-aware training (QAT) and post-training quantization (PTQ) on tasks like dynamical systems, Sequential MNIST, and Long Range Arena (LRA). They find that S5 models can maintain high accuracy (within 1% of full precision) for sMNIST and most LRA tasks, with recurrent weights being more sensitive to lower bit precisions. PTQ is effective for language tasks but not as much for others, emphasizing the need for QAT. The study highlights the trade-offs between quantization, performance, and hardware deployment, particularly in resource-constrained environments, and suggests future directions for optimizing SSMs and exploring binary activations for spatio-temporal signal coding.
Mind map
Evaluation of PTQ performance across different tasks, focusing on language vs. other tasks
Application of PTQ on S5 models
Comparison of full precision vs. quantized models for sMNIST and LRA tasks
Implementation of QAT on S5 models
Real-world scenarios and hardware deployment considerations
Resource-constrained environment implications
Trade-offs between quantization, computational cost, and memory footprint
Comparison of QAT and PTQ results
Quantized model accuracy (within 1% of full precision) for sMNIST and LRA tasks
Identification of the most affected components in the model
Recurrent weight quantization sensitivity analysis
Post-Training Quantization (PTQ)
Quantization-Aware Training (QAT)
Benchmark datasets and evaluation criteria
Selection of tasks: dynamical systems, Sequential MNIST (sMNIST), and Long Range Arena (LRA)
Identify performance trade-offs and hardware implications
Investigate quantization-aware training (QAT) and post-training quantization (PTQ)
To examine the effects of quantization on S5 models
Importance of computational efficiency in sequence modeling
Overview of State Space Models (SSMs) and the S5 model
Limitations and potential future research directions in quantized SSMs
Summary of key findings and implications for the state space modeling community
Exploration of spatio-temporal signal coding with binary activations
Suggestions for optimizing SSMs, including binary activations
Case Studies
Hardware Efficiency and Deployment
Accuracy and Performance
Sensitivity Analysis
Data Preprocessing and Quantization Techniques
Data Collection
Objective
Background
Conclusion
Future Directions
Results and Discussion
Methodology
Introduction
Outline
Introduction
Background
Overview of State Space Models (SSMs) and the S5 model
Importance of computational efficiency in sequence modeling
Objective
To examine the effects of quantization on S5 models
Investigate quantization-aware training (QAT) and post-training quantization (PTQ)
Identify performance trade-offs and hardware implications
Methodology
Data Collection
Selection of tasks: dynamical systems, Sequential MNIST (sMNIST), and Long Range Arena (LRA)
Benchmark datasets and evaluation criteria
Data Preprocessing and Quantization Techniques
Quantization-Aware Training (QAT)
Implementation of QAT on S5 models
Comparison of full precision vs. quantized models for sMNIST and LRA tasks
Post-Training Quantization (PTQ)
Application of PTQ on S5 models
Evaluation of PTQ performance across different tasks, focusing on language vs. other tasks
Sensitivity Analysis
Recurrent weight quantization sensitivity analysis
Identification of the most affected components in the model
Results and Discussion
Accuracy and Performance
Quantized model accuracy (within 1% of full precision) for sMNIST and LRA tasks
Comparison of QAT and PTQ results
Hardware Efficiency and Deployment
Trade-offs between quantization, computational cost, and memory footprint
Resource-constrained environment implications
Case Studies
Real-world scenarios and hardware deployment considerations
Future Directions
Suggestions for optimizing SSMs, including binary activations
Exploration of spatio-temporal signal coding with binary activations
Conclusion
Summary of key findings and implications for the state space modeling community
Limitations and potential future research directions in quantized SSMs
Key findings
3

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper "Q-S5: Towards Quantized State Space Models" aims to address the challenge of developing fully quantized State Space Models (SSMs) based on the S5 architecture . This research focuses on creating quantized models that achieve high accuracy comparable to full-precision models while utilizing significantly less memory and predominantly integer operations . The goal is to explore the effectiveness of Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT) for language-based tasks, such as Text and Retrieval, within the context of Large Random Access (LRA) tasks . This problem of optimizing quantized models for efficient performance is not entirely new, but the specific approach and focus on SSMs and the S5 architecture represent a novel contribution to the field of neural network quantization .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the impact of quantization on State Space Models (SSMs) and its effect on model performance, particularly focusing on the S5 model. The study investigates how quantization, through quantization-aware training (QAT) and post-training quantization (PTQ), influences the performance of SSMs across various tasks such as dynamical systems modeling, Sequential MNIST (sMNIST), and Long Range Arena (LRA) tasks. The research systematically evaluates the sensitivity of SSMs to quantization, highlighting the degradation in performance for recurrent weights below 8-bit precision and the varying impact on different components of the models .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Towards Quantized State Space Models" proposes several new ideas, methods, and models in the field of sequence modeling architectures based on State Space Models (SSMs) . Here are some key points from the paper:

  1. Quantization Sensitivity Evaluation: The paper systematically evaluates the quantization sensitivity of SSMs using quantization-aware training (QAT) and post-training quantization (PTQ) across different tasks such as dynamical systems modeling, Sequential MNIST (sMNIST), and Long Range Arena (LRA) tasks . The study shows that performance on most tasks degrades significantly for recurrent weights below 8-bit precision, while other components can be compressed further without significant loss of performance.

  2. Fully Quantized S5 Models: The paper presents fully quantized S5 models that maintain test accuracy within a 1% drop on sMNIST and most of the LRA tasks . This indicates the effectiveness of quantization in reducing memory usage and performing integer operations efficiently while maintaining model accuracy.

  3. Quantization Techniques: The study highlights that post-training quantization (PTQ) performs well on language-based LRA tasks, whereas other tasks require quantization-aware training (QAT) . This distinction in the performance of different quantization techniques based on the task type provides insights for optimizing model efficiency.

  4. Future Directions: The paper outlines future directions for research, including exploring optimal tradeoffs between training compute and final model efficiency through quantization-aware finetuning (QAFT) methods . Additionally, the authors aim to extend their analysis to demonstrate how sparse binary activations can code complex patterns in spatio-temporal signals using recurrent linear maps, similar to the approach in SSMs .

Overall, the paper introduces innovative approaches to quantization in SSMs, evaluates the impact of quantization on model performance across various tasks, and suggests future research directions to enhance the efficiency and effectiveness of quantized state space models . The paper "Towards Quantized State Space Models" introduces several characteristics and advantages of the S5 model compared to previous methods, highlighting its efficiency and suitability for dynamical systems modeling. Here are the key points based on the details in the paper:

  1. Computational Efficiency: The S5 model, based on State Space Models (SSMs), offers computational efficiency compared to transformer architectures, particularly in terms of scalability with sequence length and performance on various sequence modeling tasks . This efficiency makes SSMs like the S5 model a potent alternative to transformers for next-generation sequence modeling architectures.

  2. Quantization Sensitivity: The study systematically evaluates the quantization sensitivity of SSMs using quantization-aware training (QAT) and post-training quantization (PTQ) across different tasks such as dynamical systems modeling, Sequential MNIST (sMNIST), and Long Range Arena (LRA) tasks . The results show that while performance degrades significantly for recurrent weights below 8-bit precision, other components can be compressed further without significant loss of performance.

  3. Model Performance: Fully quantized S5 models are presented in the paper, maintaining test accuracy within a 1% drop on sMNIST and most of the LRA tasks . This indicates that the S5 model can achieve high performance even with quantization, showcasing its robustness and effectiveness in maintaining accuracy while using significantly less memory and integer operations.

  4. Quantization Techniques: The study highlights the importance of quantization-aware training (QAT) for most tasks, with post-training quantization (PTQ) performing well specifically on language-based LRA tasks . This distinction in the performance of different quantization techniques provides insights into optimizing model efficiency based on the task requirements.

  5. Future Research Directions: The paper outlines future research directions, aiming to explore optimal tradeoffs between training compute and final model efficiency through quantization-aware finetuning (QAFT) methods . Additionally, the authors plan to extend their analysis to demonstrate how sparse binary activations can code complex patterns in spatio-temporal signals using recurrent linear maps, similar to the approach in SSMs, indicating a promising direction for further advancements in the field .

Overall, the S5 model based on State Space Models offers computational efficiency, robust performance under quantization constraints, and potential for further advancements in sequence modeling architectures .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of quantized state space models. Noteworthy researchers in this area include:

  • AmirAli Abdolrashidi, Lisa Wang, Shivani Agrawal, Jonathan Malmaud, Oleg Rybakov, Chas Leichner, and Lukasz Lew .
  • Joachim Ott, Zhouhan Lin, Ying Zhang, Shih-Chii Liu, and Yoshua Bengio .
  • Jens Egholm Pedersen, J¨org Conradt, and Tony Lindeberg .
  • Sumit Bam Shrestha, Jonathan Timcheck, Paxon Frady, Leobardo Campos-Macias, and Mike Davies .
  • Yehui Tang, Yunhe Wang, Jianyuan Guo, Zhijun Tu, Kai Han, Hailin Hu, and Dacheng Tao .

The key to the solution mentioned in the paper involves conducting experiments using quantization-aware training (QAT) and post-training quantization (PTQ). In QAT, the model is trained with dynamic quantization, while in PTQ, a full-precision model is quantized without training .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of various quantization configurations on tasks such as sMNIST and LRA tasks. The experiments involved testing different quantization setups, including PTQ and QAT, with variations in precision levels for weights and activations . Additionally, the paper included ablation studies to assess the impact of quantized operators like the quantized GELU activation function, hard sigmoid, and quantized layer norm operation on model performance . The results of the experiments were presented in tables showing test accuracies for different models and configurations .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided context . Regarding the open-source status of the code used in the research, the information about the code being open source is not provided in the context as well.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that need to be verified. The study systematically evaluates the impact of quantization on State Space Models (SSMs) across various tasks like dynamical systems modeling, Sequential MNIST (sMNIST), and Long Range Arena (LRA) tasks . The results show that the performance of the fully quantized S5 models only drops by less than 1% on sMNIST and most LRA tasks, indicating the effectiveness of quantization in maintaining model accuracy .

Furthermore, the study highlights that the performance degradation is significant for recurrent weights below 8-bit precision, while other components can be compressed further without a notable loss of performance . This finding supports the hypothesis that the precision of quantization plays a crucial role in the performance of SSMs. Additionally, the results demonstrate that post-training quantization (PTQ) performs well on language-based LRA tasks, whereas quantization-aware training (QAT) is required for other tasks, providing valuable insights into the optimal quantization methods for different types of tasks .

Overall, the experiments and results in the paper offer strong empirical evidence to support the scientific hypotheses related to the impact of quantization on State Space Models, shedding light on the quantization sensitivity of SSMs across various tasks and providing essential insights for the development of efficient and hardware-optimized SSMs .


What are the contributions of this paper?

The paper "Q-S5: Towards Quantized State Space Models" makes several contributions:

  • It explores efficient modeling of long sequences with structured state spaces .
  • The paper discusses the parameterization and initialization of diagonal state space models .
  • It presents research on methods and theories of quantized neural networks .
  • The study highlights the effectiveness of diagonal state spaces compared to structured state spaces .
  • It delves into the topic of quantization methods for efficient neural network inference .
  • The paper introduces the concept of linear-time sequence modeling with selective state spaces .
  • It provides insights into recurrent memory with optimal polynomial projections .
  • The research presents results from experiments on tasks like sMNIST and LRA, showcasing test accuracies for various models and techniques .
  • The study compares the performance of different quantization methods such as PTQ, QAT, and QAFT, showing that QAT outperforms PTQ and QAFT in certain scenarios .

What work can be continued in depth?

To further advance the research in quantized state space models, several areas of work can be continued in depth based on the provided document :

  • Optimal Tradeoffs in Training Compute and Model Efficiency: Future research can explore optimal tradeoffs between training compute and final model efficiency to enhance the performance of quantized state space models.
  • Quantization-Aware Finetuning (QAFT): Expanding QAFT methods can lead to improved model efficiency and performance, especially for language-based tasks like Text and Retrieval.
  • Exploration of Large Pre-Trained Selective SSMs: There is a potential to extend QAFT methods to large pre-trained selective state space models such as Mamba, Jamba, and Griffin, to further enhance their efficiency and effectiveness.
  • Sparse Binary Activations for Complex Patterns: The analysis can be extended to demonstrate how sparse binary activations (spikes) can encode complex patterns in spatio-temporal signals using linear kernels, similar to the approach in state space models.

By delving deeper into these areas, researchers can advance the understanding and application of quantized state space models for various tasks and domains.

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