Abstract
Working memory enables the brain to integrate transient information for rapid decision-making. Artificial networks typically replicate this via recurrent or parallel architectures, yet incur high energy costs and noise sensitivity. Here we report IPNet, a hardware-software co-designed neuromorphic architecture realizing human-like working memory via neuronal intrinsic plasticity. Exploiting Joule-heating dynamics of Magnetic Tunnel Junctions (MTJs), IPNet physically emulates biological memory volatility. The memory behavior of the proposed architecture shows similar trends in n-back, free recall and memory interference tasks to that of reported human subjects. Implemented exclusively with MTJ neurons, the architecture with human-like working memory achieves 99.65% accuracy on 11-class DVS gesture datasets and maintains 99.48% on a novel 22-class time-reversed benchmark, outperforming RNN, LSTM, and 2+1D CNN baselines sharing identical backbones. For autonomous driving (DDD-20), IPNet reduces steering prediction error by 14.4% compared to ResNet-LSTM. Architecturally, we identify a 'Memory-at-the-Frontier' effect where performance is maximized at the sensing interface, validating a bio-plausible near-sensor processing paradigm. Crucially, all results rely on raw parameters from fabricated devices without optimization. Hardware-in-the-loop validation confirms the system's physical realizability. Separately, energy analysis reveals a reduction in memory power of 2,874x compared to LSTMs and 90,920x versus parallel 3D-CNNs. This capacitor-free design enables a compact ~1.5um2 footprint (28 nm CMOS): a >20-fold reduction over standard LIF neurons. Ultimately, we demonstrate that instantiating human-like working memory via intrinsic neuronal plasticity endows neural networks with the dual biological advantages of superior dynamic vision processing and minimal metabolic cost.
Abstract (translated)
工作记忆使大脑能够整合瞬时信息，以实现快速决策。人工网络通常通过递归或并行架构来模拟这一过程，但这样会带来高昂的能量消耗和噪声敏感性。在此，我们报道了一种名为IPNet的神经形态架构，它通过神经元固有可塑性实现了类似于人类的工作记忆。利用磁隧道结（MTJ）中的焦耳热动力学，IPNet物理上模仿了生物记忆的波动特性。该架构的记忆行为在n-back、自由回忆和记忆干扰任务中表现出与报告的人类受试者相似的趋势。 仅使用MTJ神经元实现的具有类似人类工作记忆的架构，在11类DVS手势数据集上实现了99.65%的准确率，并且在22类时间反转基准测试上保持了99.48%的准确率，优于具有相同骨干网络的RNN、LSTM和2+1D CNN基线。对于自动驾驶（DDD-20），IPNet将转向预测误差降低了14.4%，相较于ResNet-LSTM。 从架构上看，我们发现了一个“前沿记忆”效应，在感知接口处性能最大化，验证了生物合理且接近传感器的处理范式。关键的是，所有结果都依赖于未经优化的设备原始参数。硬件在环测试证实系统的物理实现可行性。独立的能量分析揭示与LSTMs相比内存功率降低2,874倍，与并行3D-CNNs相比降低了90,920倍。 这种无电容设计实现了约1.5um²的小尺寸（基于28nm CMOS工艺），比标准的LIF神经元小了超过20倍。最终，我们证明通过固有神经可塑性实现类似于人类的工作记忆赋予神经网络在动态视觉处理和最低代谢成本方面的双重生物学优势。
URL
https://arxiv.org/abs/2512.15829
