InstaTrain: Adaptive Training via Ultra-Fast Natural Annealing within Dynamical Systems
Time-Series Prediction;Online Learning;Nature-Powered Computing
Yes
No ethics review needed.
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.
* 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.
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.
* 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.
Yes
No ethics review needed.
See the weaknesses part.
- 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.
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.
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.
Yes
No ethics review needed.
Can you add more comparison results in the form of tables?
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.
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.
- 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.
Yes
No ethics review needed.
see weakness
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.
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.
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.
A novel ultra-rapid learning paradigm for time-series prediction tasks through iterative natural annealing within dynamical systems.
@inproceedings{
anonymous2024instatrain,
title={InstaTrain: Adaptive Training via Ultra-Fast Natural Annealing within Dynamical Systems},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=QhhShUQIpJ},
note={under review}
}
Time-series modeling is broadly adopted to capture underlying patterns and trends present in historical data, allowing for prediction of future values. However, one crucial aspect in such modeling is often overlooked: in highly dynamic environments, data distributions can shift drastically within a second or less. Under this circumstance, traditional predictive models, even online learning methods struggle to adapt to the ultra-fast and complex distribution shift present in highly dynamic scenarios. To address this, we propose InstaTrain, a novel learning paradigm that enables frequent model updates with microsecond-level intervals for real-world prediction tasks, allowing it to keep pace with rapidly evolving data distributions. In this work, (1) We transform the slow and expensive model training process into an ultra-fast natural annealing process that can be carried out on a dynamical system. (2) Leveraging a recently proposed electronic dynamical system, we augment the system with a parameter update module, extending its capabilities to encompass both rapid training and inference. Experimental results across highly dynamic datasets demonstrate that our method delivers on average, a remarkable 4,135$\times$ training speedup, $10^5\times$ reduction in training energy costs, and 69.1\% / 45.8\% lower test MAE over the best result of SOTA methods running on GPUs with / without the online learning mechanism.
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Time-Series Prediction Online Learning Nature-Powered Computing
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learning on time series and dynamical systems
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InstaTrain: Adaptive Training via Ultra-Fast Natural Annealing within Dynamical Systems
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