Jingli Liu

Huannan Zheng, Jingli Liu, Kezhou Yang

Signed spiking neurons carry richer information than standard spiking neurons. This work proposes a compact magnetic tunnel junction (MTJ)-based neuron for signed leaky integrate-and-fire (LIF) operation. With orthogonal easy axes in the free and pinned layers, the device enables bipolar spike generation and maps magnetic-moment dynamics to signed LIF membrane-potential evolution. Landau--Lifshitz--Gilbert simulations show that proper free-layer dimensions allow the device response to follow a signed LIF equation. A representative design of 10 nm x 45 nm x 50 nm corresponds to an aspect ratio of about 2:9:10. Network evaluations using the fitted device-neuron model achieve 91.06% on CIFAR-10 and 77.40% on CIFAR10-DVS, retaining most of the accuracy of ideal signed LIF neurons.

Jingli Liu, Huannan Zheng, Bohao Zou et al.

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.