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This paper introduces a structured memory which can be easily integrated intoa neural network. The memory is very large by design and significantlyincreases the capacity of the architecture, by up to a billion parameters witha negligible computational overhead. Its design and access pattern is based onproduct keys, which enable fast and exact nearest neighbor search. The abilityto increase the number of parameters while keeping the same computationalbudget lets the overall system strike a better trade-off between predictionaccuracy and computation efficiency both at training and test time. This memorylayer allows us to tackle very large scale language modeling tasks. In ourexperiments we consider a dataset with up to 30 billion words, and we plug ourmemory layer in a state-of-the-art transformer-based architecture. Inparticular, we found that a memory augmented model with only 12 layersoutperforms a baseline transformer model with 24 layers, while being twicefaster at inference time. We release our code for reproducibility purposes.

Large Memory Layers with Product Keys