Journal
IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS
Volume 30, Issue 1, Pages 68-80Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVLSI.2021.3100252
Keywords
Compute-in-memory (CIM); insect-scale drones; probabilistic localization
Funding
- Intel
- National Science Foundations (NSF) [2046435]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [2046435] Funding Source: National Science Foundation
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A novel ultralow-power framework is proposed for probabilistic localization of insect-scale drones, which is 25 times more energy-efficient than traditional processor, paving the way for tiny autonomous drones.
We propose a novel compute-in-memory (CIM)-based ultralow-power framework for probabilistic localization of insect-scale drones. Localization is a critical subroutine for path planning and rotor control in drones, where a drone is required to continuously estimate its pose (position and orientation) in flying space. The conventional probabilistic localization approaches rely on the 3-D Gaussian mixture model (GMM)-based representation of a 3-D map. A GMM model with hundreds of mixture functions is typically needed to adequately learn and represent the intricacies of the map. Meanwhile, localization using complex GMM map models is computationally intensive. Since insect-scale drones operate under extremely limited area/power budget, continuous localization using GMM models entails much higher operating energy, thereby limiting flying duration and/or size of the drone due to a larger battery. Addressing the computational challenges of localization in an insect-scale drone using a CIM approach, we propose a novel framework of 3-D map representation using a harmonic mean of the Gaussian-like mixture (HMGM) model. We show that short-circuit current of a multiinput floating-gate CMOS-based inverter follows the harmonic mean of a Gaussian-like function. Therefore, the likelihood function useful for drone localization can be efficiently implemented by connecting many multiinput inverters in parallel, each programmed with the parameters of the 3-D map model represented as HMGM. When the depth measurements are projected to the input of the implementation, the summed current of the inverters emulates the likelihood of the measurement. We have characterized our approach on an RGB-D scenes dataset. The proposed localization framework is similar to 25x energy-efficient than the traditional, 8-bit digital GMM-based processor paving the way for tiny autonomous drones.
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