Spiking neural networks excel at event-driven sensing. Yet, maintaining task-relevant context over long timescales both algorithmically and in hardware, while respecting both tight energy and memory budgets, remains a core challenge in the field. We address this challenge through an algorithm-hardware co-design effort. At the algorithm level, inspired by the cortical fast-slow organization in the brain, we introduce a neural network with an explicit slow memory pathway that, combined with fast spiking activity, enables a dual memory pathway (DMP) architecture in which each layer maintains a compact low-dimensional state that summarizes recent activity and modulates spiking dynamics. This explicit memory stabilizes learning while preserving event-driven sparsity, achieving competitive accuracy on long-sequence benchmarks with 40-60% fewer parameters than equivalent state-of-the-art spiking neural networks. At the hardware level, we introduce a near-memory-compute architecture that fully leverages the advantages of the DMP architecture by retaining its compact shared state while optimizing dataflow, across heterogeneous sparse-spike and dense-memory pathways. We show experimental results that demonstrate more than a 4X increase in throughput and over a 5X improvement in energy efficiency compared with state-of-the-art implementations. Together, these contributions demonstrate that biological principles can guide functional abstractions that are both algorithmically effective and hardware-efficient, establishing a scalable co-design framework for real-time neuromorphic computation and learning.

翻译：脉冲神经网络在事件驱动感知方面表现出色。然而，如何在算法和硬件层面同时兼顾严格的能量与内存预算，在长时间尺度上维持任务相关上下文，仍是该领域的核心挑战。我们通过算法-硬件协同设计应对这一挑战。在算法层面，受大脑皮质快慢组织结构的启发，我们引入了一种包含显式慢记忆通路的神经网络，与快速脉冲活动相结合，实现了双记忆通路（DMP）架构——每一层维护一个紧凑的低维状态，该状态汇总近期活动并调节脉冲动态。这种显式记忆在保持事件驱动稀疏性的同时稳定了学习过程，在长序列基准测试中达到了具有竞争力的精度，且参数量比同等最先进的脉冲神经网络减少40-60%。在硬件层面，我们提出了一种近存计算架构，通过保留DMP架构紧凑的共享状态并优化数据流，在异构的稀疏脉冲通路和密集记忆通路间充分释放其优势。实验结果表明，与最先进的实现相比，吞吐量提升超过4倍，能效提升超过5倍。这些贡献共同证明，生物学原理能够指导兼具算法效能和硬件效率的功能抽象，为实时神经形态计算与学习建立了一种可扩展的协同设计框架。