# Assessing the Performance of Analog Training for Transfer Learning

arXiv: https://arxiv.org/abs/2505.11067 · HTML: https://mlsanity.com/paper/2505.11067/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 33 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving](https://mlsanity.com/paper/2310.01957/) (2023)
- [Large Language Models in Finance: A Survey](https://mlsanity.com/paper/2311.10723/) (2023)
- [Detecting LLM-Generated Text in Computing Education: A Comparative Study for ChatGPT Cases](https://mlsanity.com/paper/2307.07411/) (2023)
- [Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices](https://mlsanity.com/paper/1603.07341/) (2016)
- [Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence](https://mlsanity.com/paper/2106.01288/) (2021)
- [Algorithm for Training Neural Networks on Resistive Device Arrays](https://mlsanity.com/paper/1909.07908/) (2019)
- [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)
- [A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks](https://mlsanity.com/paper/2104.09382/) (2021)
- [Training large-scale ANNs on simulated resistive crossbar arrays](https://mlsanity.com/paper/1906.02698/) (2019)

## Built on by (most cited first, 1 of 1)

- [Making Analog Training Scale: Co-Designing Mapping, Optimizer, and Converters](https://mlsanity.com/paper/2609.36584/) (2026)

## Abstract (arXiv)

Analog in-memory computing is a next-generation computing paradigm that promises fast, parallel, and energy-efficient deep learning training and transfer learning (TL). However, achieving this promise has remained elusive due to a lack of suitable training algorithms. Analog memory devices exhibit asymmetric and non-linear switching behavior in addition to device-to-device variation, meaning that most, if not all, of the current off-the-shelf training algorithms cannot achieve good training outcomes. Also, recently introduced algorithms have enjoyed limited attention, as they require bi-directionally switching devices of unrealistically high symmetry and precision and are highly sensitive. A new algorithm chopped TTv2 (c-TTv2), has been introduced, which leverages the chopped technique to address many of the challenges mentioned above. In this paper, we assess the performance of the c-TTv2 algorithm for analog TL using a Swin-ViT model on a subset of the CIFAR100 dataset. We also investigate the robustness of our algorithm to changes in some device specifications, including weight transfer noise, symmetry point skew, and symmetry point variability