NeuroFlow 55.8x video inference speedup for Vision Transformers PyTorch
Official PyTorch implementation of NeuroFlow: EMA-Gated Temporal Sequence Compression for Vision Transformers. Achieves up to 55.8x wall-clock speedup for video inference via semantic surprise routing and a training-free Dual-Memory Reconstruction Protocol. - ynnk-research/-NeuroFlow
NeuroFlow 55.8x video inference speedup for Vision Transformers PyTorch launched on May 26, 2026 with 8 votes, #1,430 of 20,538 launches that month and more than 75% of that year's launches.
What it does
TL;DR: Vision Transformers waste 90% of their compute recalculating stationary asphalt. NeuroFlow tracks semantic surprise in embedding space, physically eliminating background tokens before the encoder. Left: Standard SigLIP FP16 processing 100% of tokens (~722ms) @1792p. Right: NeuroFlow Architecture B eliminating 98% of stationary tokens before the encoder (~15ms) @1792p. This repository contains the official PyTorch implementation, verification scripts, and pre-print paper for NeuroFlow . NeuroFlow is a dynamic routing framework for Vision Transformer video inference. It exploits temporal redundancy by tracking per-patch embedding-distance threshold via an Exponential Moving Average…from github.com
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