# NeoNeXt: Novel neural network operator and architecture based on the patch-wise matrix multiplications

Source: [NeoNeXt: Novel neural network operator and architecture based on the patch-wise matrix multiplications](<https://www.sota2.com/research/paper/68fa72971e209131cea575dd>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:29:52.311Z

Authors: Vladimir Korviakov, Denis Koposov

Published: 2024-03-17T15:51:21.000Z

## Abstract

Most of the computer vision architectures nowadays are built upon the well-known foundation operations: fully-connected layers, convolutions and multi-head self-attention blocks. In this paper we propose a novel foundation operation - NeoCell - which learns matrix patterns and performs patchwise matrix multiplications with the input data. The main advantages of the proposed operator are (1) simple implementation without need in operations like im2col, (2) low computational complexity (especially for large matrices) and (3) simple and flexible implementation of up-/down-sampling. We validate NeoNeXt family of models based on this operation on ImageNet-1K classification task and show that they achieve competitive quality.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.11251>)

[PDF](<https://arxiv.org/pdf/2403.11251.pdf>)