While the unsustainable energy cost of artificial intelligence necessitates physics-driven computing, its performance superiority over full-precision GPUs remains a challenge. We bridge this gap by repurposing the Joule-heating relaxation dynamics of magnetic tunnel junctions, conventionally suppressed as noise, into neuronal intrinsic plasticity, realizing working memory with human-like features. Traditional AI utilizes energy-intensive digital memory that accumulates historical noise in dynamic environments. Conversely, our Intrinsic Plasticity Network (IPNet) leverages thermodynamic dissipation as a temporal filter. We provide direct system-level evidence that this physics-driven memory yields an 18x error reduction compared to spatiotemporal convolutional models in dynamic vision tasks, reducing memory-energy overhead by >90,000x. In autonomous driving, IPNet reduces prediction errors by 12.4% versus recurrent networks. This establishes a neuromorphic paradigm that shatters efficiency limits and surpasses conventional algorithmic performance.

翻译：尽管人工智能的不可持续能源成本迫使人们转向物理驱动计算，但其在性能上超越全精度GPU仍是一项挑战。我们通过将磁隧道结中通常被抑制为噪声的焦耳热弛豫动力学重新利用为神经元内在可塑性，从而弥合这一差距，实现了具有类人特征的工作记忆。传统AI使用高能耗的数字记忆，在动态环境中会累积历史噪声。相反，我们的内在可塑性网络（IPNet）利用热力学耗散作为时间滤波器。我们提供了直接的系统级证据，表明在动态视觉任务中，这种物理驱动记忆相比时空卷积模型减少了18倍的误差，并将记忆能量开销降低超过90,000倍。在自动驾驶中，IPNet相比循环网络将预测误差降低了12.4%。这建立了一种神经形态范式，打破了效率极限并超越了传统算法性能。