用神经形态硬件实现低能耗鲁棒拟合，效率提升显著。
Event-driven Robust Fitting on Neuromorphic Hardware
- 设计事件驱动的脉冲神经网络，在Loihi 2上实现鲁棒拟合。
- 在同等精度下，能耗仅为传统CPU算法的15%。
- 适合关注低功耗视觉计算的开发者与研究者。
鲁棒几何模型拟合是计算机视觉中的基础任务。尽管已有大量创新提升采样效率与理论可靠性，但能源效率仍被忽视。随着人工智能能耗问题日益突出，本文探索基于神经形态计算的高效鲁棒拟合。我们设计了一种新型脉冲神经网络，在真实神经形态硬件Intel Loihi 2上实现鲁棒拟合，采用事件驱动的模型估计公式，并结合算法策略缓解硬件精度与指令集限制。实验表明，在达到同等精度时，本方法能耗仅为标准CPU上现有鲁棒拟合算法的15%。
原文摘要 · Abstract (English)
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.
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