Abstract
Simultaneous localization and mapping (SLAM) is a quintessential problem in autonomous navigation, augmented reality, and virtual reality. In particular, low-power SLAM has gained increasing importance for its applications in power-limited edge devices such as unmanned aerial vehicles (UAVs) and small-sized cars that constitute devices with edge intelligence. This article presents a 7.25-to-8.79-TOPS/W mixed-signal oscillator-based SLAM accelerator for applications in edge robotics. This study proposes a neuromorphic SLAM IC, called NeuroSLAM, employing oscillator-based pose-cells and a digital head direction cell to mimic place cells and head direction cells that have been discovered in a rodent brain. The oscillatory network emulates a spiking neural network and its continuous attractor property achieves spatial cognition with a sparse energy distribution, similar to the brains of rodents. Furthermore, a lightweight vision system with a max-pooling is implemented to support low-power visual odometry and re-localization. The test chip fabricated in a 65-nm CMOS exhibits a peak energy efficiency of 8.79 TOPS/W with a power consumption of 23.82 mW.
Original language | English |
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Article number | 9222208 |
Pages (from-to) | 66-78 |
Number of pages | 13 |
Journal | IEEE Journal of Solid-State Circuits |
Volume | 56 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2021 |
Bibliographical note
Publisher Copyright:© 1966-2012 IEEE.
Keywords
- Accelerator
- continuous attractor network
- edge intelligence
- experience map
- simultaneous localization and mapping (SLAM)
- spiking neural network (SNN)
- topological map
- visual odometry
- visual template (VT)