Gleanmer: A 6 mW SoC for Real-Time 3D Gaussian Occupancy Mapping
arXiv:2603.29005v1 Announce Type: new Abstract: High-fidelity 3D occupancy mapping is essential for many edge-based applications (such as AR/VR and autonomous navigation) but is limited by power constraints. We present Gleanmer, a system on chip (SoC) with an accelerator for GMMap, a 3D occupancy map using Gaussians. Through algorithm-hardware co-optimizations for direct computation and efficient reuse of these compact Gaussians, Gleanmer reduces construction and query energy by up to 63% and 81%, respectively. Approximate computation on Gaussians reduces accelerator area by 38%. Using 16nm CMOS, Gleanmer processes 640x480 images in real time beyond 88 fps during map construction and processes over 540K coordinates per second during map query. To our knowledge, Gleanmer is the first fabric
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Abstract:High-fidelity 3D occupancy mapping is essential for many edge-based applications (such as AR/VR and autonomous navigation) but is limited by power constraints. We present Gleanmer, a system on chip (SoC) with an accelerator for GMMap, a 3D occupancy map using Gaussians. Through algorithm-hardware co-optimizations for direct computation and efficient reuse of these compact Gaussians, Gleanmer reduces construction and query energy by up to 63% and 81%, respectively. Approximate computation on Gaussians reduces accelerator area by 38%. Using 16nm CMOS, Gleanmer processes 640x480 images in real time beyond 88 fps during map construction and processes over 540K coordinates per second during map query. To our knowledge, Gleanmer is the first fabricated SoC to achieve real-time 3D occupancy mapping under 6 mW for edge-based applications.
Comments: Accepted to IEEE Symposium on VLSI Technology & Circuits (VLSI), 2026. To appear
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2603.29005 [cs.RO]
(or arXiv:2603.29005v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2603.29005
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zih-Sing Fu [view email] [v1] Mon, 30 Mar 2026 21:08:52 UTC (3,526 KB)
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