Recently, there are increasing efforts on advancing optical neural networks (ONNs), which bring significant advantages for machine learning (ML) in terms of power efficiency, parallelism, and computational speed. With the considerable benefits in computation speed and energy efficiency, there are significant interests in leveraging ONNs into medical sensing, security screening, drug detection, and autonomous driving. However, due to the challenge of implementing reconfigurability, deploying multi-task learning (MTL) algorithms on ONNs requires re-building and duplicating the physical diffractive systems, which significantly degrades the energy and cost efficiency in practical application scenarios. This work presents a novel ONNs architecture, namely, \textit{RubikONNs}, which utilizes the physical properties of optical systems to encode multiple feed-forward functions by physically rotating the hardware similarly to rotating a \textit{Rubik's Cube}. To optimize MTL performance on RubikONNs, two domain-specific physics-aware training algorithms \textit{RotAgg} and \textit{RotSeq} are proposed. Our experimental results demonstrate more than 4$\times$ improvements in energy and cost efficiency with marginal accuracy degradation compared to the state-of-the-art approaches.

翻译：近年来，光学神经网络（ONNs）的发展日益增多，其在机器学习（ML）中具有功耗效率、并行性和计算速度等方面的显著优势。凭借计算速度和能效上的巨大优势，ONNs在医疗传感、安全检查、药物检测和自动驾驶等领域引起了广泛关注。然而，由于实现可重构性的挑战，在ONNs上部署多任务学习（MTL）算法需要重新构建和复制物理衍射系统，这在实际应用场景中显著降低了能量和成本效率。本文提出了一种新颖的ONNs架构，即\textit{RubikONNs}，它利用光学系统的物理特性，通过物理旋转硬件（类似于旋转\textit{魔方}）来编码多个前馈函数。为了优化RubikONNs上的MTL性能，提出了两种领域特定的物理感知训练算法\textit{RotAgg}和\textit{RotSeq}。我们的实验结果表明，与最先进的方法相比，能量和成本效率提高了4倍以上，同时精度下降微乎其微。