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DTSTAMP:20260402T024534Z
LOCATION:3000\, Level 3
DTSTART;TZID=America/Los_Angeles:20250625T161500
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UID:dac_DAC 2025_sess112_RESEARCH2474@linklings.com
SUMMARY:GSAcc: Accelerate 3D Gaussian Splatting via Depth Speculation and 
 Gaussian-centric Rasterization
DESCRIPTION:Mengtian Yang, Yipeng Wang, Chieh-Pu Lo, Xiuhao Zhang, Sirish 
 Oruganti, and Jaydeep Kulkarni (The University of Texas at Austin)\n\n3D G
 aussian Splatting (3DGS) has emerged as a promising real-time photorealist
 ic radiance field rendering technique.  Existing GPU and hardware accelera
 tors face limitations due to insufficient parallelism in sequential render
 ing pipeline stages and the memory overhead associated with interim result
 s. This paper presents GSAcc, a hardware accelerator co-designed with data
 flow to render compressed 3DGS models on edge platforms efficiently. GSAcc
  enhances 3DGS rendering performance through several key innovations. Firs
 t, it introduces Gaussian depth speculation, parallelizing preprocessing a
 nd sorting tasks. Second, GSAcc adopts a Gaussian-centric dataflow that in
 terleaves preprocessing and rasterization, allowing all rendering steps to
  execute concurrently without storing intermediate results. Finally, it em
 ploys dedicated hardware acceleration to address sorting and rasterization
  bottlenecks within the optimized dataflow. We implemented and synthesized
  GSAcc using Intel16 PDK and evaluated its performance on real-world 3DGS 
 scenes. Compared with desktop GPUs, GSAcc achieves up to 1.66x10^4x Power-
 Performance-Area (PPA) improvement as well as 48.7x energy savings. Additi
 onally, GSAcc outperforms the state-of-the-art hardware accelerator GSCore
  with up to 2.3x PPA improvement and 2.9x energy savings.\n\nTopics: AI\n\
 nTracks: AI3: AI/ML Architecture Design\n\nSession Chairs: Debjyoti Bhatta
 charjee (imec) and Minah Lee (Georgia Institute of Technology)\n\n
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