M2RU用忆阻器实现低功耗边缘持续学习，支持实时自适应。
M2RU: Memristive Minion Recurrent Unit for On-Chip Continual Learning at the Edge
- 采用混合信号架构与加权位流处理，减少数据移动和高精度转换能耗。
- 在顺序MNIST和CIFAR-10任务上保持95%以上基线准确率，能效达312 GOPS/W。
- 适合边缘设备上的实时时序智能应用，尤其关注低功耗持续学习场景。
边缘平台上的持续学习仍具挑战性，因循环网络依赖高能耗训练与频繁数据传输，难以部署于嵌入式系统。本文提出M2RU，一种混合信号架构，实现基于忆阻器的微型循环单元，用于高效时序处理与片上持续学习。该架构引入加权位流技术，使多比特数字输入可在交叉阵列中直接处理，无需高分辨率转换；并集成经验回放机制，在领域漂移下稳定学习。M2RU实现15 GOPS算力，功耗48.62 mW，能效达312 GOPS每瓦，且在顺序MNIST与CIFAR-10任务中准确率保持在软件基线的5%以内。相比CMOS数字设计，能效提升29倍。器件级分析显示，在持续学习负载下预期工作寿命达12.2年。结果表明M2RU是边缘时序智能中可扩展、低功耗实时自适应的理想平台。
原文摘要 · Abstract (English)
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.
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