InstaTrain: Adaptive Training via Ultra-Fast Natural Annealing within Dynamical Systems

Authors: Chuan Liu, Ruibing Song, Chunshu Wu, Pouya Haghi, Tong Geng
Primary area: learning on time series and dynamical systems
Keywords: Time-Series Prediction, Online Learning, Novel Computing Paradigm

Abstract:
Time-series modeling is broadly adopted to capture underlying patterns present in historical data, allowing prediction of future values. However, one crucial aspect of such modeling is often overlooked: in highly dynamic environments, data distributions can shift drastically within a second or less. Under these circumstances, traditional predictive models, and even online learning methods, struggle to adapt to the ultra-fast and complex distribution shifts present in highly dynamic scenarios. To address this, we propose InstaTrain, a novel learning approach that enables ultra-fast model updates for real-world prediction tasks, thereby keeping pace with rapidly evolving data distributions. In this work, (1) we transform the slow and expensive training process into an ultra-fast natural annealing process within a dynamical system. (2) Leveraging a recently proposed electronic dynamical system, we augment the system with parameter update modules, extending its capabilities to encompass both rapid training and inference. Experimental results on highly dynamic datasets demonstrate that our method achieves orders-of-magnitude improvements in training speed and energy efficiency while delivering superior accuracy compared to baselines running on GPUs.

Decision: accept (poster)
Reviewer scores: 8, 6, 5, 6

Reviews and discussion:
<thread>
creation date:
1730606196802

thread_type:
official_review

<thread_object>
creator: reviewer_3cwj

<content>
summary:
The paper introduces InstaTrain, a novel adaptive learning paradigm designed to operate in highly dynamic environments where data distributions shift rapidly. InstaTrain leverages a natural annealing process on an electronic dynamical system, enabling model training and updates at microsecond intervals. Experimental results across high-frequency datasets showcase substantial improvements in speed, energy efficiency, and prediction accuracy compared to state-of-the-art (SOTA) methods.

strengths:
1. The application of natural annealing as a training mechanism in a dynamical system is novel, addressing limitations in existing adaptive models in terms of speed and energy consumption.

2. InstaTrain demonstrates significant training speedups (4,135×) and energy reductions (105×) while achieving superior accuracy on high-frequency datasets. This supports the paper’s claims of real-time adaptability and efficiency.

3. The integration of parameter update modules within the dynamical system facilitates simultaneous training and inference, a promising approach for handling rapid data shifts.

weaknesses:
1. The model appears to primarily handle binary data (+1, -1). How does the proposed InstaTrain method address continuous data in time series applications?

2. The paper omits several widely used time series datasets, including ETTm1, Electricity, Traffic, Exchange, and Weather.

questions:
see weakness
</content>
</thread_object>

</thread>
<thread>
creation date:
1730650767216

thread_type:
official_review

<thread_object>
creator: reviewer_hpwn

<content>
summary:
This paper introduces InstaTrain, a novel approach for ultra-fast model training in highly dynamic environments where data distributions can change within microseconds. Traditional models, including online learning approaches, struggle to adapt quickly enough to such rapid changes. The authors transform the typically slow training process into an ultra-fast "natural annealing" process implemented on an electronic dynamical system. Their key contribution is twofold: they develop a new training algorithm called Iterative Natural Annealing based Training (INAT) that reformulates model training as an energy minimization problem, and they augment existing electronic hardware with parameter update modules to enable both training and inference on the same system. Evaluated on three high-frequency datasets including stock market and chemical sensor data, InstaTrain achieves dramatic improvements over state-of-the-art methods, demonstrating orders of magnitude faster training and inference speeds, significantly lower energy consumption, and better prediction accuracy. The work effectively bridges physics-inspired computing with machine learning to address the challenge of rapid distribution shifts in real-world applications.

strengths:
Originality: The paper creatively combines statistical physics with machine learning in a novel way, transforming traditional model training into a physical annealing process. While both Ising models and online learning existed previously, extending electronic hardware to support microsecond-level training and inference together represents a genuine innovation. The work effectively removes the speed limitations that have constrained previous approaches to dynamic model adaptation.
Quality: The technical work is rigorous, with thorough mathematical foundations and experimental validation. The evaluation compares against multiple baselines across several high-frequency datasets, demonstrating improvements in speed, energy efficiency, and accuracy. The inclusion of ablation studies further strengthens the empirical results.
Clarity: The paper effectively communicates complex concepts through clear writing.
Significance: The work has substantial practical impact by enabling model adaptation at previously impossible speeds while reducing energy costs by orders of magnitude. It also opens promising research directions by demonstrating how physics-based computing can be effectively combined with machine learning to overcome fundamental limitations of traditional approaches.

weaknesses:
- The baseline comparison is incomplete. The paper should compare to more regular time series prediction models.
- The presentation of the results in bar plot is not ideal. Tables of numbers are much better.

questions:
Can you add more comparison results in the form of tables?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730719875352

thread_type:
official_review

<thread_object>
creator: reviewer_2k7c

<content>
summary:
This paper presents a novel approach that transforms traditional model training into an iterative natural annealing process within an electronic dynamical system. The proposed approach operates through Iterative Natural Annealing-based Training (INAT), which performs training directly on an electronic dynamical system rather than on conventional processors. The key innovation lies in extending existing electronic dynamical system hardware from inference-only to support both training and inference on the same hardware. The authors claim significant improvements in training speed, energy efficiency, and prediction accuracy compared to state-of-the-art methods, particularly for time-series prediction tasks with rapidly evolving data distributions.

strengths:
- The novel transformation of traditional training into a natural annealing process is interesting.
- Hardware-algorithm co-design that extends existing electronic dynamical systems.
- Clear theoretical foundation.
- Well-structured presentation with clear problem motivation.

weaknesses:
1. Some concerns about the experimental datasets. As far as I know, the three datasets used are time series data with relatively low-dimension. There is no discussion of scalability to larger, more complex datasets. How would the system perform on higher-dimensional data or more complex tasks? Are there theoretical or practical limitations?

2. The experimental results of the proposed IntraTrain approach are based on CUDA-based software simulator rather than actual hardware implementation. Although the author claimed that the real hardware has been manufactured in the reproducibility part.

3. In Table 1, the power consumption of InstaTrain (950mW) is actually higher than baseline BRIM (250mW) and NP-GL (260mW), could you provide detailed calculations for the claimed energy reduction?

4. The paper compares performance with "not achievable" baseline results for high-frequency scenarios. Could you explain the methodology behind these comparisons since they are "not achievable"?

I'd be happy to raise the score if the author could address my concerns.

questions:
See the weaknesses part.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730876440628

thread_type:
official_review

<thread_object>
creator: reviewer_vhtd

<content>
summary:
The manuscript proposes an improved electronic dynamical system---InstraTrain---to tackle the rapidly evolving nature of time series in a highly dynamic environment: it enables frequent model updates with microsecond-level intervals for real-world predictions.

The manuscript extends the extraordinary computational efficiency of the electronic dynamical system from inference to training, using two major components. It first formulates the time series prediction problem via the natural annealing procedure of a dynamical system, enabling performing efficient prediction. It further considers a physical realization of the proposed dynamical system.

strengths:
* The manuscript is well-written. It starts from the statistical mechanics in physics and models the time-series prediction problem as a dynamical system. It then leverages the natural annealing process of a dynamical system to enable ultra-fast model training. Furthermore, it redesigns the electronic dynamical system from a hardware aspect, enabling the self-training feature. The reviewer enjoys reading the paper, especially section 3.
* The manuscript considers three types of baselines, covering 7 methods. It also uses an FEA software simulator to measure the accuracy and latency of the proposed method. Numerical results show that InstaTrain can achieve a significant speedup over all baseline models, while maintaining a $10^5$ reduction in energy consumption.

weaknesses:
* The datasets used for the evaluation are not carefully explained, making the significance of the performance gain unclear. It would be great if the authors could elaborate on the dataset statistics. It would be great if the authors could include results on more challenging datasets.
* One intuition of the paper is that the algorithm could converge to the equilibrium state of the dynamical system. It would be great if the authors could justify this point, either theoretical or empirical.

questions:
In lines 365-366, the authors claim that "the more accurate high-frequency update is unattainable due to the sluggish training speed of NP-GL, GNNs, and even SOTA online-training methods", as well as the "'Not achievable' are due to slow training" in the caption of Figure 5, the reviewer is a bit confused regarding these results, as they are not achievable. The authors are encouraged to explain this point.
</content>
</thread_object>

</thread>
<thread>
creation date:
1733257846814

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Summary of Rebuttal and Discussion

comment:
Dear Area Chair and Reviewers, 

We sincerely appreciate all the reviewers for their constructive feedback and insightful suggestions throughout the review process. We are pleased to note the positive outcomes from the discussion phase: 

- Reviewer VHTd upheld the high score. Their suggestions are insightful, and we have addressed each point thoroughly with the responses incorporated in the revision.  
- Reviewer 2k7C maintained their positive score, recognizing that our revisions effectively addressed their concerns. While the score was not raised to 8, their comments describing our paper as 'a good paper' are encouraging. 
- Reviewer 3cwj increased the score following our clarifications on how InstaTrain addresses continuous data and the additional evaluation on other widely used datasets. We thank the reviewer for recognizing InstaTrain as a novel and promising approach. 
- Reviewer hPWn provided valuable suggestions to add more baselines and additional evaluations on other widely used datasets. We have addressed each point thoroughly with our responses incorporated in the revision. We appreciate the reviewer for recognizing the high originality, quality, clarity, and significance of our work in their review. 

As the discussion concludes, we would like to summarize the key points addressed: 

1. Enhanced Comparison and Evaluation.  
- In response to Reviewer VHTd and Reviewer 2k7C, we conducted further experiments on more challenging datasets. The results, detailed in Table 1, Figure 5, and Figure 6, highlight InstaTrain's superior performance on high-dimensional datasets.  
- Addressing Reviewer hPWn’s request, we included three new SOTA baselines in Table 1, Figure 5, and Figure 6, further validating the strengths of InstaTrain.  
- Following the suggestions of Reviewer 3cwj and Reviewer hPWn, we evaluated InstaTrain’s performance on additional widely used datasets, as presented in Appendix A.3, providing a more comprehensive evaluation. 
2. Improved Explanation and Presentation.  
- To address the question from Reviewer VHTd and Reviewer 2k7C regarding the "Not Achievable" results, we provided a detailed explanation of why baseline methods fail to achieve optimal results in high-frequency scenarios, thereby highlighting InstaTrain’s advantages in these settings.  
- Responding to Reviewer VHTd’s request, we included both theoretical and empirical justifications for the convergence to the equilibrium state within the dynamical system.  
- In response to Reviewer 2k7C’s inquiry, we provided a detailed explanation of the calculations involved in energy reduction, ensuring clarity.  
- To address Reviewer 3cwj’s questions, we elaborated on how InstaTrain effectively manages continuous real-valued data within the Background section, ensuring a comprehensive understanding of our approach.  
- Following Reviewer hPWn’s recommendation, we added more tables to present our results, facilitating easier comparison and interpretation of the data. 

We would like to emphasize the **contributions** of InstaTrain as recognized by reviewers: 

- **A new fast and low-cost training method:** InstaTrain enables a novel type of efficient training approach by transforming the traditional expensive and slow training process on digital processors into fast and low-cost natural annealing processes within dynamical systems, achieving orders-of-magnitude speedup in model training and updating compared to baselines.  

- **Expanding the capability of an emerging AI processor:** InstaTrain extends the exceptional computational potential of the emerging dynamical system computing substrate from inference-only to training, opening a new research direction for efficient AI training. 

- **Improving accuracy for high-frequency ML challenges:** With the microsecond-level model update capability, InstaTrain provides significantly improved accuracy, particularly in tackling complex high-frequency ML problems. 

Finally, we thank the reviewers for their invaluable comments, which have strengthened our work. We hope InstaTrain provides meaningful insights and advances the development of high-speed and low-cost AI training. 

Best Regards,

The Authors
</content>
</thread_object>

</thread>


Metareview:
metareview:
This paper outlines a system for time-series model training in environments where data distributions can change within microseconds. The authors further augment existing electronic hardware with parameter update modules to enable both training and inference on the same system. The experimental results reported on several high-frequency datasets including stock market and chemical sensor data, show improvements over the state-of-the-art. Most reviewers voted for acceptance. The authors provided an extensive rebuttal to address the concerns raised by the reviewers. I would encourage the authors to incorporate those results and insights in the final version/additional appendices to further strengthen the paper.