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Instant on-device Neural Radiance Fields (NeRFs) are in growing demand forunleashing the promise of immersive AR/VR experiences, but are still limited bytheir prohibitive training time. Our profiling analysis reveals a memory-boundinefficiency in NeRF training. To tackle this inefficiency, near-memoryprocessing (NMP) promises to be an effective solution, but also faceschallenges due to the unique workloads of NeRFs, including the random hashtable lookup, random point processing sequence, and heterogeneous bottlenecksteps. Therefore, we propose the first NMP framework, Instant-NeRF, dedicatedto enabling instant on-device NeRF training. Experiments on eight datasetsconsistently validate the effectiveness of Instant-NeRF.

Instant-NeRF: Instant On-Device Neural Radiance Field Training via Algorithm-Accelerator Co-Designed Near-Memory Processing