Instant neural graphics primitives with a multiresolution hash encoding

Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate. We leverage this parallelism by implementing the whole system using fully-fused CUDA kernels with a focus on minimizing wasted bandwidth and compute operations.

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• 1 We reduce this cost with a versatile new input encoding that permits the use of a smaller network without sacrificing quality, thus significantly reducing the number of floating point and memory access operations: a small neural network is augmented by a multiresolution hash table of trainable feature vectors whose values are optimized through stochastic gradient descent.
• 2 The multiresolution structure allows the network to disambiguate hash collisions, making for a simple architecture that is trivial to parallelize on modern GPUs.
• 3 We leverage this parallelism by implementing the whole system using fully-fused CUDA kernels with a focus on minimizing wasted bandwidth and compute operations.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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