Attention Approximates Sparse Distributed MemoryDownload PDF

May 21, 2021 (edited Nov 09, 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: Transformer, Attention, Sparse Distributed Memory, Associative Memory, Cerebellum
  • TL;DR: We show the heuristic Transformer Attention operation can be implemented with simple properties of high dimensional vectors, in a biologically plausible fashion using SDM.
  • Abstract: While Attention has come to be an important mechanism in deep learning, there remains limited intuition for why it works so well. Here, we show that Transformer Attention can be closely related under certain data conditions to Kanerva's Sparse Distributed Memory (SDM), a biologically plausible associative memory model. We confirm that these conditions are satisfied in pre-trained GPT2 Transformer models. We discuss the implications of the Attention-SDM map and provide new computational and biological interpretations of Attention.
  • Supplementary Material: pdf
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  • Code: https://github.com/TrentBrick/attention-approximates-sdm
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