Radiant Foam Rendering on a Graph Processor
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Abstract (arXiv)
Many emerging many-core accelerators replace a single large device memory with hundreds to thousands of lightweight cores, each owning only a small local SRAM and exchanging data via explicit on-chip communication. This organization offers high aggregate bandwidth, but it breaks a key assumption behind many volumetric rendering techniques: that rays can randomly access a large, unified scene representation. Rendering efficiently on such hardware therefore requires distributing both data and computation, keeping ray traversal mostly local, and structuring communication into predictable routes. We present a fully in-SRAM, distributed renderer for the Radiant Foam Voronoi-cell volumetric representation on the Graphcore Mk2 IPU(Intelligence Processing Unit), a many-core accelerator with tile-local SRAM and explicit inter-tile communication. Our system shards the scene across tiles and forwards rays between shards through a hierarchical routing overlay, enabling ray marching entirely from on-chip SRAM with predictable communication. On Mip-NeRF~360 scenes, the system attains near-interactive throughput of approximately 1 fps at 640x480 with image and depth map quality close to the original GPU-based Radiant Foam implementation, while keeping all scene data and ray state in on-chip SRAM. Beyond demonstrating feasibility, we analyze routing, memory, and scheduling bottlenecks that inform how future distributed-memory accelerators can better support irregular, data-movement-heavy rendering workloads.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 25 of its 53 references that are in the map. First posted on arXiv 7 Jan 2026.
Builds on
25 of 53 references.
References · 25
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13 more
- 2022RT-NeRF: Real-Time On-Device Neural Radiance Fields Towards Immersive AR/VR Renderingcited by 35
- 2024Sparse Voxels Rasterization: Real-time High-fidelity Radiance Field Renderingcited by 35
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- 2020Bundle Adjustment on a Graph Processorcited by 15
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- 2020Graphcore C2 Card performance for image-based deep learning application: A Reportcited by 5
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Citing papers per year: 2026: 1.
1 paper.
- 2026Rendering 3D Gaussians on a Graph Processorcited by 0
- 2026Rendering 3D Gaussians on a Graph Processor17 Jul 2026