Memristive devices present a promising foundation for next-generation information processing by combining memory and computation within a single physical substrate. This unique characteristic enables efficient, fast, and adaptive computing, particularly well suited for deep learning applications. Among recent developments, the memristive-friendly echo state network (MF-ESN) has emerged as a promising approach that combines memristive-inspired dynamics with the training simplicity of reservoir computing, where only the readout layer is learned. Building on this framework, we propose memristive-friendly parallelized reservoirs (MARS), a simplified yet more effective architecture that enables efficient scalable parallel computation and deeper model composition through novel subtractive skip connections. This design yields two key advantages: substantial training speedups of up to 21x over the inherently lightweight echo state network baseline and significantly improved predictive performance. Moreover, MARS demonstrates what is possible with parallel memristive-friendly reservoir computing: on several long sequence benchmarks our compact gradient-free models substantially outperform strong gradient-based sequence models such as LRU, S5, and Mamba, while reducing full training time from minutes or hours down seconds or even only a few hundred milliseconds. Our work positions parallel memristive-friendly computing as a promising route towards scalable neuromorphic learning systems that combine high predictive capability with radically improved computational efficiency, while providing a clear pathway to energy-efficient, low-latency implementations on emerging memristive and in-memory hardware.

翻译：忆阻器件通过将存储与计算集成于同一物理基底，为下一代信息处理奠定了有前景的基础。这一独特特性使其能够实现高效、快速、自适应的计算，特别适用于深度学习应用。在近期进展中，忆阻友好型回声状态网络通过结合忆阻启发的动力学与仅需训练读出层的储层计算简易性，已成为一种颇具前景的方法。基于此框架，我们提出忆阻友好型并行化储层——一种更简化且更高效的架构，通过新型减法跳跃连接实现高效可扩展的并行计算与深度模型组合。该设计带来两大关键优势：相对于本已轻量化的回声状态网络基准，训练速度提升高达21倍，同时显著提高预测性能。此外，MARS展示了并行忆阻友好型储层计算的潜力：在多个长序列基准测试中，我们的紧凑型无梯度模型大幅优于LRU、S5、Mamba等强梯度序列模型，并将完整训练时间从数分钟或数小时缩短至数秒甚至仅数百毫秒。本工作将并行忆阻友好型计算定位为迈向可扩展神经形态学习系统的有前景路径——此类系统兼具高预测能力与根本性提升的计算效率，同时为在新型忆阻与存内硬件上实现高能效、低延迟部署提供了清晰路线图。