论文标题
用于储层计算的物理实现的短波长回响系统
Short-wavelength Reverberant Wave Systems for Physical Realization of Reservoir Computing
论文作者
论文摘要
机器学习（ML）在广泛的重要任务中发现了广泛的应用。为了提高ML绩效，研究人员研究了计算体系结构，其物理实现有望紧凑，高速执行，身体鲁棒性和低能成本。在这里，我们在实验上展示了一种使用回响短波长波的高灵敏度，以实现和增强一种称为储层计算的ML类型的计算能力（RC）。 RC系统的潜在计算能力随其有效尺寸而增加。我们在这里利用短波长回响波灵敏度对扰动的内在特性，通过空间和光谱扰动来扩大RC系统的有效尺寸。该方案在微波制度中工作，对不同的ML任务进行了实验测试。我们的结果表明，基于Reverant Wave的RC实现以及我们有效的储层尺寸扩展技术的总体适用性
Machine learning (ML) has found widespread application over a broad range of important tasks. To enhance ML performance, researchers have investigated computational architectures whose physical implementations promise compactness, high-speed execution, physical robustness, and low energy cost. Here, we experimentally demonstrate an approach that uses the high sensitivity of reverberant short wavelength waves for physical realization and enhancement of computational power of a type of ML known as reservoir computing (RC). The potential computation power of RC systems increases with their effective size. We here exploit the intrinsic property of short wavelength reverberant wave sensitivity to perturbations to expand the effective size of the RC system by means of spatial and spectral perturbations. Working in the microwave regime, this scheme is tested experimentally on different ML tasks. Our results indicate the general applicability of reverberant wave-based implementations of RC and of our effective reservoir size expansion technique
