Robust fitting of geometric models is a fundamental task in many computer vision pipelines. Numerous innovations have been produced on the topic, from improving the efficiency and accuracy of random sampling heuristics to generating novel theoretical insights that underpin new approaches with mathematical guarantees. However, one aspect of robust fitting that has received little attention is energy efficiency. This performance metric has become critical as high energy consumption is a growing concern for AI adoption. In this paper, we explore energy-efficient robust fitting via the neuromorphic computing paradigm. Specifically, we designed a novel spiking neural network for robust fitting on real neuromorphic hardware, the Intel Loihi 2. Enabling this are novel event-driven formulations of model estimation that allow robust fitting to be implemented in the unique architecture of Loihi 2, and algorithmic strategies to alleviate the current limited precision and instruction set of the hardware. Results show that our neuromorphic robust fitting consumes only a fraction (15%) of the energy required to run the established robust fitting algorithm on a standard CPU to equivalent accuracy.

翻译：几何模型的鲁棒拟合是计算机视觉流程中的一项基础任务。该领域已涌现诸多创新，从提升随机采样启发式算法的效率与精度，到产生支撑具有数学保证的新方法的理论洞见。然而，鲁棒拟合的能效问题却鲜有关注。随着高能耗日益成为人工智能应用推广的关键制约因素，这一性能指标已变得至关重要。本文通过神经形态计算范式探索能效鲁棒拟合。具体而言，我们设计了一种新颖的脉冲神经网络，用于在真实的神经形态硬件（Intel Loihi 2）上实现鲁棒拟合。实现这一目标的关键在于：提出了新颖的事件驱动模型估计公式，使得鲁棒拟合能够在Loihi 2的独特架构中得以实施；并设计了算法策略以缓解该硬件当前有限的精度和指令集约束。实验结果表明，在达到同等精度的情况下，我们的神经形态鲁棒拟合方法仅消耗标准CPU上运行成熟鲁棒拟合算法所需能量的很小一部分（15%）。