基于磁流体的神经网络：模拟联想存储器的设计
A ferrofluid based neural network: design of an analogue associative memory
摘要：
我们分析了基于铁磁流体的关联存储器，该铁磁流体由悬浮在可变粘度的载体流体中的磁性纳米颗粒的系统组成，该载体流体受到来自输入和输出磁垫阵列的磁场图案的影响。该关联依赖于在训练阶段期间在铁磁流体中形成图案，其中磁偶极子自由移动和旋转以使系统的总能量最小化。一旦对于给定的输入-输出磁场图案对能量平衡，通过冷却载液使颗粒完全或部分固定。由此产生的粒子分布控制存储器状态，其使用并入输出垫中的自旋阀传感器磁性地读出。实际的记忆由自旋分布组成，自旋分布本质上是动态的，仅在响应系统已被训练的输入模式时才实现。研究了两种用于存储多个模式的训练算法。使用物理系统的蒙特卡罗模拟，我们证明了该设备能够存储和调用两组图像，每一组的准确度接近100%。
英文摘要：
We analyse an associative memory based on a ferrofluid, consisting of a system of magnetic nano-particles suspended in a carrier fluid of variable viscosity subject to patterns of magnetic fields from an array of input and output magnetic pads. The association relies on forming patterns in the ferrofluid during a trainingdphase, in which the magnetic dipoles are free to move and rotate to minimize the total energy of the system. Once equilibrated in energy for a given input-output magnetic field pattern-pair the particles are fully or partially immobilized by cooling the carrier liquid. Thus produced particle distributions control the memory states, which are read out magnetically using spin-valve sensors incorporated in the output pads. The actual memory consists of spin distributions that is dynamic in nature, realized only in response to the input patterns that the system has been trained for. Two training algorithms for storing multiple patterns are investigated. Using Monte Carlo simulations of the physical system we demonstrate that the device is capable of storing and recalling two sets of images, each with an accuracy approaching 100%.
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