NeuroSLAM: A 65-nm 7.25-to-8.79-TOPS/W Mixed-Signal Oscillator-Based SLAM Accelerator for Edge Robotics

Jong Hyeok Yoon, Arijit Raychowdhury

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

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 languageEnglish
Article number9222208
Pages (from-to)66-78
Number of pages13
JournalIEEE Journal of Solid-State Circuits
Volume56
Issue number1
DOIs
StatePublished - 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)

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