Kim Gwangmin, In Jae Hyun, Lee Younghyun, Rhee Hakseung, Park Woojoon, Song Hanchan, Park Juseong, Jeon Jae Bum, Brown Timothy D, Talin A Alec, Kumar Suhas, Kim Kyung Min
Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Sandia National Laboratories, Livermore, CA, USA.
Nat Mater. 2024 Sep;23(9):1237-1244. doi: 10.1038/s41563-024-01913-0. Epub 2024 Jun 18.
Heat dissipation is a natural consequence of operating any electronic system. In nearly all computing systems, such heat is usually minimized by design and cooling. Here, we show that the temporal dynamics of internally produced heat in electronic devices can be engineered to both encode information within a single device and process information across multiple devices. In our demonstration, electronic NbO Mott neurons, integrated on a flexible organic substrate, exhibit 18 biomimetic neuronal behaviours and frequency-based nociception within a single component by exploiting both the thermal dynamics of the Mott transition and the dynamical thermal interactions with the organic substrate. Further, multiple interconnected Mott neurons spatiotemporally communicate purely via heat, which we use for graph optimization by consuming over 10 times less energy when compared with the best digital processors. Thus, exploiting natural thermal processes in computing can lead to functionally dense, energy-efficient and radically novel mixed-physics computing primitives.
散热是任何电子系统运行的自然结果。在几乎所有的计算系统中,这种热量通常通过设计和冷却来降至最低。在此,我们表明,电子设备内部产生的热量的时间动态特性可以被设计成既能在单个设备内编码信息,又能在多个设备间处理信息。在我们的演示中,集成在柔性有机基板上的电子铌氧化物莫特神经元,通过利用莫特转变的热动力学以及与有机基板的动态热相互作用,在单个组件内展现出18种仿生神经元行为和基于频率的痛觉感受。此外,多个相互连接的莫特神经元纯粹通过热量进行时空通信,与最佳数字处理器相比,我们利用这种方式进行图优化时消耗的能量减少了10倍以上。因此,在计算中利用自然热过程可导致功能密集、节能且全新的混合物理计算原语。