AI 中文总结
研究自然界无全局协调下异步计算之谜，核心方法是将计算物理‘硬件’分两部分，通过惯性推动异步计算，贡献包括用理论绘制相图、实验验证及构建软件，指出能量受限下可靠分散计算的通用策略。
AI 中文摘要
计算是状态的可控演化。异步演化中状态各部分在各自时间变化且互不干扰，这使控制面临风险。自然界如何在无全局协调的情况下用众多单元进行异步计算仍是谜。本文展示了异步多体系统中集体计算能力如何出现。关键是将计算的物理‘硬件’分为两个非对称耦合部分，类似谐振子中的位置和动量。由此产生的惯性推动状态演化，使异步计算按正确顺序进行。将惯性异步计算机视为非平衡物质，用循环动力学平均场理论对其相图进行数值和解析绘制。通过模拟大脑实际神经元的模拟脉冲神经形态芯片进行实验验证，并构建了可在异步硬件上运行的软件，能对训练中未见过的干净版本电影去噪。结果指出了在能量受限环境中从动力学自组装到细胞分化进行可靠、分散计算的通用策略。
英文摘要
Computation is the controlled evolution of a state. Asynchronous evolutions, where all parts of the state change in their own time without stopping each other, put this control in jeopardy. It is in fact a mystery how natural processes perform asynchronous computations using many units with no global orchestration. Here we demonstrate how collective computational abilities can emerge in asynchronous many-body systems. The key insight is to split the physical "hardware" underlying the computation into two asymmetrically coupled parts, analogous to position and momentum in a harmonic oscillator. The resulting inertia nudges the evolution of the state so that the asynchronous computation proceeds in the right order. By treating our inertial asynchronous computer as a nonequilibrium material, we map out its phase diagram numerically and analytically using a framework we dub loop dynamical mean-field theory. We experimentally demonstrate our approach using analog spiking neuromorphic chips designed to mimic actual neurons in the brain. In addition, we construct software that can run on asynchronous hardware: we denoise movies whose clean versions were never seen during training, an instantiation of the generalization transition underlying modern machine learning. Our results point to a general strategy for reliable, decentralized computation in energy-constrained settings from dynamics self-assembly to cell differentiation.
