The rapidly increasing computational demands for artificial intelligence (AI) have spurred the exploration of computing principles beyond conventional digital computers. Physical neural networks (PNNs) offer efficient neuromorphic information processing by harnessing the innate computational power of physical processes; however, training their weight parameters is computationally expensive. We propose a training approach for substantially reducing this training cost. Our training approach merges an optimal control method for continuous-time dynamical systems with a biologically plausible training method--direct feedback alignment. In addition to the reduction of training time, this approach achieves robust processing even under measurement errors and noise without requiring detailed system information. The effectiveness was numerically and experimentally verified in an optoelectronic delay system. Our approach significantly extends the range of physical systems practically usable as PNNs.

翻译：人工智能（AI）计算需求的快速增长推动了对超越传统数字计算机的计算原理的探索。物理神经网络（PNNs）通过利用物理过程固有的计算能力，实现了高效的神经形态信息处理；然而，训练其权重参数的计算成本高昂。我们提出了一种显著降低此训练成本的训练方法。该方法将连续时间动力学系统的最优控制方法与一种生物可塑性训练方法——直接反馈对齐——相结合。除了减少训练时间外，该方法即使在测量误差和噪声存在的情况下也能实现鲁棒处理，且无需详细的系统信息。其有效性在光电子延迟系统中得到了数值和实验验证。我们的方法显著扩展了可实际用作PNNs的物理系统范围。