A 51.2 fps Real-Time 3DGS-SLAM Accelerator using Diagonal Feeding with Symmetric Alpha Reuse and Voxel-based 3D Gaussian Cache Management

This work presents a high-speed 3D Gaussian Splatting-based SLAM (3DGS-SLAM) accelerator to support dense mapping for mobile devices. 3DGS-SLAM has two main hardware challenges for acceleration: 1) Large α-computation computation. 2) Memory bottleneck caused by irregular memory access and large number of Gaussians. First, diagonal feeding (DF) controller precludes redundant-α computation, and symmetric alpha reuse (SAR) enables reusing computed alpha. This method reduces 35.3% system computation. Second, voxel-based inter-frame caching (VIFC) enables selective inter-frame voxel caching, which reduces 44.0% of external memory access. As a result, the proposed 3DGS-SLAM accelerator achieves 51.2 fps with 0.07µJ/point with support voltage 0.9V, clock frequency 200MHz mapping a high-quality dense-map.