⚡ 本页包含 AI 生成的分析内容，仅供参考
该论文提出了一种基于混合信号振荡器的神经SLAM加速器，用于边缘机器人中的超低功耗同时定位与地图构建（SLAM）问题。通过创新的电路设计，实现了8.79 TOPS/W的能效和23.82 mW的功耗，显著降低了边缘机器人的能耗。
Simultaneous localization and mapping (SLAM) is a quintessential problem in cyber-physical systems with wide-spread applications in mobile robotics, selfdriving vehicles, AR, VR, etc. While computational methods [1] and hardware demonstrations [2-5] based on filtering or keyframe techniques are popular, we recognize that ultra-low-power edge-robotics requires circuit solutions that will significantly reduce the power consumption. Interestingly, biological systems can solve SLAM with extreme energy-efficiencies by employing methods that are robust, flexible, and well-integrated into the creatures’ sensory systems. Particularly, rodents have shown an extraordinary ability to store and organize visual cues so that a brief sequence of visual cues can globally re-localize the animal. Further, the neural recordings of the rodent hippocampus have led to the discovery of place cells and head direction cells which show striking correlation
Jong-Hyeok Yoon, Arijit Raychowdhury
Georgia Institute of Technology, Atlanta, GA