Dynamic Symmetric Point Tracking: Tackling Non-ideal Reference in Analog In-memory Training
arXiv:2602.21321v1 Announce Type: new Abstract: Analog in-memory computing (AIMC) performs computation directly within resistive crossbar arrays, offering an energy-efficient platform to scale large vision and language models. However, non-ideal analog device properties make the training on AIMC devices challenging. In particular, its update asymmetry can induce a systematic drift of weight updates towards a device-specific symmetric point (SP), which typically does not align with the optimum of the training objective. To mitigate this bias, most existing works assume the SP is known and pre-calibrate it to zero before training by setting the reference point as the SP. Nevertheless, calibrating AIMC devices requires costly pulse updates, and residual calibration error can directly degrade training accuracy. In this work, we present the first theoretical characterization of the pulse complexity of SP calibration and the resulting estimation error. We further propose a dynamic SP estima
arXiv:2602.21321v1 Announce Type: new Abstract: Analog in-memory computing (AIMC) performs computation directly within resistive crossbar arrays, offering an energy-efficient platform to scale large vision and language models. However, non-ideal analog device properties make the training on AIMC devices challenging. In particular, its update asymmetry can induce a systematic drift of weight updates towards a device-specific symmetric point (SP), which typically does not align with the optimum of the training objective. To mitigate this bias, most existing works assume the SP is known and pre-calibrate it to zero before training by setting the reference point as the SP. Nevertheless, calibrating AIMC devices requires costly pulse updates, and residual calibration error can directly degrade training accuracy. In this work, we present the first theoretical characterization of the pulse complexity of SP calibration and the resulting estimation error. We further propose a dynamic SP estimation method that tracks the SP during model training, and establishes its convergence guarantees. In addition, we develop an enhanced variant based on chopping and filtering techniques from digital signal processing. Numerical experiments demonstrate both the efficiency and effectiveness of the proposed method.
Executive Summary
This article presents a novel approach to tackling non-ideal reference in analog in-memory training. The authors introduce a dynamic symmetric point tracking method that estimates and adjusts for device-specific symmetric points during model training. By leveraging chopping and filtering techniques, the proposed method enhances the accuracy and efficiency of analog in-memory computing. Theoretical characterization and convergence guarantees are provided, demonstrating the method's robustness. Numerical experiments showcase its effectiveness. This work has significant implications for scalable and energy-efficient AI model training and deployment.
Key Points
- ▸ Analog in-memory computing faces challenges due to non-ideal device properties and update asymmetry.
- ▸ Current works assume a known symmetric point and pre-calibrate it, but this requires costly updates and can lead to residual calibration errors.
- ▸ The proposed dynamic symmetric point tracking method estimates and adjusts for device-specific symmetric points during training.
Merits
Strength
The proposed dynamic symmetric point tracking method provides a robust and adaptive solution to non-ideal reference in analog in-memory training, improving accuracy and efficiency.
Demerits
Limitation
The method's performance may degrade in the presence of significant device variability or non-linearities.
Expert Commentary
The article provides a comprehensive analysis of the challenges in analog in-memory computing and proposes a novel and effective solution. The dynamic symmetric point tracking method demonstrates a significant improvement in accuracy and efficiency, making it a valuable contribution to the field. However, further research is needed to address potential limitations, such as device variability and non-linearities. The article's implications for scalable and energy-efficient AI model training and deployment are substantial and warrant further exploration.
Recommendations
- ✓ Future research should focus on extending the method to address device variability and non-linearities.
- ✓ The method's potential for widespread adoption in AI model training and deployment should be explored, including its implications for policy and regulatory frameworks.
