# Zero-shifting Technique for Deep Neural Network Training on Resistive Cross-point Arrays

arXiv: https://arxiv.org/abs/1907.10228 · HTML: https://mlsanity.com/paper/1907.10228/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 7 of 7)

- [Fast offset corrected in-memory training](https://mlsanity.com/paper/2303.04721/) (2023)
- [Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference](https://mlsanity.com/paper/2307.09357/) (2023)
- [Analog In-memory Training on General Non-ideal Resistive Elements: The Impact of Response Functions](https://mlsanity.com/paper/2502.06309/) (2025)
- [Neural Network Training with Asymmetric Crosspoint Elements](https://mlsanity.com/paper/2201.13377/) (2022)
- [Dynamic Symmetric Point Tracking: Tackling Non-ideal Reference in Analog In-memory Training](https://mlsanity.com/paper/2602.21321/) (2026)
- [Making Analog Training Scale: Co-Designing Mapping, Optimizer, and Converters](https://mlsanity.com/paper/2609.36584/) (2026)
- [Mitigating Overfitting in Graph Neural Networks via Feature and Hyperplane Perturbation](https://mlsanity.com/paper/2211.15081/) (2022)

## Abstract (arXiv)

A resistive memory device-based computing architecture is one of the promising platforms for energy-efficient Deep Neural Network (DNN) training accelerators. The key technical challenge in realizing such accelerators is to accumulate the gradient information without a bias. Unlike the digital numbers in software which can be assigned and accessed with desired accuracy, numbers stored in resistive memory devices can only be manipulated following the physics of the device, which can significantly limit the training performance. Therefore, additional techniques and algorithm-level remedies are required to achieve the best possible performance in resistive memory device-based accelerators. In this paper, we analyze asymmetric conductance modulation characteristics in RRAM by Soft-bound synapse model and present an in-depth analysis on the relationship between device characteristics and DNN model accuracy using a 3-layer DNN trained on the MNIST dataset. We show that the imbalance between up and down update leads to a poor network performance. We introduce a concept of symmetry point and propose a zero-shifting technique which can compensate imbalance by programming the reference device and changing the zero value point of the weight. By using this zero-shifting method, we show that network performance dramatically improves for imbalanced synapse devices.