Presentation
SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity
DescriptionRendering is critical in fields like 3D modeling, AR/VR, and autonomous driving, where high-quality, real-time output is essential. Point-based neural rendering (PBNR) offers an efficient, photorealistic alternative to traditional methods, yet it is still challenging to achieve real-time PBNR on mobile platforms. We pinpoint the LOD search stage as a key bottleneck due to workload imbalance and irregular memory access. To address this, we propose SLTarch, an algorithm-architecture co-designed system including SLTree, a novel subtree-based data structure along with LTCore, a co-designed architecture. Compared to a mobile GPU, SLTarch achieves 2.2x speedup and 56% energy savings with negligible architecture overhead. With additional hardware support, SLTarch further boosts the performance to 3.4x with 99% energy savings.
Event Type
Networking
Work-in-Progress Poster
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby
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