Attention Approximates Sparse Distributed Memory

Trenton Bricken, Cengiz Pehlevan

Keywords: Transformer, Attention, Sparse Distributed Memory, Associative Memory, Cerebellum

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.

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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.

Code: https://github.com/TrentBrick/attention-approximates-sdm

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/arxiv:2111.05498/code)