@inproceedings{chen-etal-2025-edgeinfinite,
title = "{E}dge{I}nfinite: A Memory-Efficient Infinite-Context Transformer for Edge Devices",
author = "Chen, Jiyu and
Peng, Shuang and
Luo, Daxiong and
Yang, Fan and
Wu, Renshou and
Li, Fangyuan and
Chen, Xiaoxin",
editor = "Rehm, Georg and
Li, Yunyao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-industry.40/",
doi = "10.18653/v1/2025.acl-industry.40",
pages = "568--575",
ISBN = "979-8-89176-288-6",
abstract = "Transformer-based large language models (LLMs) encounter challenges in processing long sequences on edge devices due to the quadratic complexity of attention mechanisms and growing memory demands from Key-Value (KV) cache. Existing KV cache optimizations struggle with irreversible token eviction in long-output tasks, while alternative sequence modeling architectures prove costly to adopt within established Transformer infrastructure. We present EdgeInfinite, a memory-efficient solution for infinite contexts that integrates compressed memory into Transformer-based LLMs through a trainable memory-gating module. This approach maintains full compatibility with standard Transformer architectures, requiring fine-tuning only a small part of parameters, and enables selective activation of the memory-gating module for long and short context task routing. The experimental result shows that EdgeInfinite achieves comparable performance to baseline Transformer-based LLM on long context benchmarks while optimizing memory consumption and time to first token."
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<abstract>Transformer-based large language models (LLMs) encounter challenges in processing long sequences on edge devices due to the quadratic complexity of attention mechanisms and growing memory demands from Key-Value (KV) cache. Existing KV cache optimizations struggle with irreversible token eviction in long-output tasks, while alternative sequence modeling architectures prove costly to adopt within established Transformer infrastructure. We present EdgeInfinite, a memory-efficient solution for infinite contexts that integrates compressed memory into Transformer-based LLMs through a trainable memory-gating module. This approach maintains full compatibility with standard Transformer architectures, requiring fine-tuning only a small part of parameters, and enables selective activation of the memory-gating module for long and short context task routing. The experimental result shows that EdgeInfinite achieves comparable performance to baseline Transformer-based LLM on long context benchmarks while optimizing memory consumption and time to first token.</abstract>
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%0 Conference Proceedings
%T EdgeInfinite: A Memory-Efficient Infinite-Context Transformer for Edge Devices
%A Chen, Jiyu
%A Peng, Shuang
%A Luo, Daxiong
%A Yang, Fan
%A Wu, Renshou
%A Li, Fangyuan
%A Chen, Xiaoxin
%Y Rehm, Georg
%Y Li, Yunyao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-288-6
%F chen-etal-2025-edgeinfinite
%X Transformer-based large language models (LLMs) encounter challenges in processing long sequences on edge devices due to the quadratic complexity of attention mechanisms and growing memory demands from Key-Value (KV) cache. Existing KV cache optimizations struggle with irreversible token eviction in long-output tasks, while alternative sequence modeling architectures prove costly to adopt within established Transformer infrastructure. We present EdgeInfinite, a memory-efficient solution for infinite contexts that integrates compressed memory into Transformer-based LLMs through a trainable memory-gating module. This approach maintains full compatibility with standard Transformer architectures, requiring fine-tuning only a small part of parameters, and enables selective activation of the memory-gating module for long and short context task routing. The experimental result shows that EdgeInfinite achieves comparable performance to baseline Transformer-based LLM on long context benchmarks while optimizing memory consumption and time to first token.
%R 10.18653/v1/2025.acl-industry.40
%U https://aclanthology.org/2025.acl-industry.40/
%U https://doi.org/10.18653/v1/2025.acl-industry.40
%P 568-575
Markdown (Informal)
[EdgeInfinite: A Memory-Efficient Infinite-Context Transformer for Edge Devices](https://aclanthology.org/2025.acl-industry.40/) (Chen et al., ACL 2025)
ACL