# M2RU: Memristive Minion Recurrent Unit for On-Chip Continual Learning at the Edge

arXiv: https://arxiv.org/abs/2512.17299 · HTML: https://mlsanity.com/paper/2512.17299/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 43 references that are in the map.
- First posted on arXiv 19 Dec 2025.

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

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [An Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditions](https://mlsanity.com/paper/2204.08817/) (2022)
- [Minimal Gated Unit for Recurrent Neural Networks](https://mlsanity.com/paper/1603.09420/) (2016)
- [A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays](https://mlsanity.com/paper/2110.10486/) (2021)
- [PANTHER: A Programmable Architecture for Neural Network Training Harnessing Energy-efficient ReRAM](https://mlsanity.com/paper/1912.11516/) (2019)
- [Multiscale Co-Design Analysis of Energy, Latency, Area, and Accuracy of a ReRAM Analog Neural Training Accelerator](https://mlsanity.com/paper/1707.09952/) (2017)
- [LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms](https://mlsanity.com/paper/2311.11420/) (2023)
- [End-to-End Memristive HTM System for Pattern Recognition and Sequence Prediction](https://mlsanity.com/paper/2006.11958/) (2020)
- [Extension of Direct Feedback Alignment to Convolutional and Recurrent Neural Network for Bio-plausible Deep Learning](https://mlsanity.com/paper/2006.12830/) (2020)
- [Time-Series Forecasting and Sequence Learning Using Memristor-based Reservoir System](https://mlsanity.com/paper/2405.13347/) (2024)
- [Exploiting Symmetric Temporally Sparse BPTT for Efficient RNN Training](https://mlsanity.com/paper/2312.09391/) (2023)
- [Minion Gated Recurrent Unit for Continual Learning](https://mlsanity.com/paper/2503.06175/) (2025)
- [Voltage Mode Winner-Take-All Circuit for Neuromorphic Systems](https://mlsanity.com/paper/2507.04338/) (2025)

## Abstract (arXiv)

Continual learning on edge platforms remains challenging because recurrent networks depend on energy-intensive training procedures and frequent data movement that are impractical for embedded deployments. This work introduces M2RU, a mixed-signal architecture that implements the minion recurrent unit for efficient temporal processing with on-chip continual learning. The architecture integrates weighted-bit streaming, which enables multi-bit digital inputs to be processed in crossbars without high-resolution conversion, and an experience replay mechanism that stabilizes learning under domain shifts. M2RU achieves 15 GOPS at 48.62 mW, corresponding to 312 GOPS per watt, and maintains accuracy within 5 percent of software baselines on sequential MNIST and CIFAR-10 tasks. Compared with a CMOS digital design, the accelerator provides 29X improvement in energy efficiency. Device-aware analysis shows an expected operational lifetime of 12.2 years under continual learning workloads. These results establish M2RU as a scalable and energy-efficient platform for real-time adaptation in edge-level temporal intelligence.