arXiv AI July 14, 2026 By Jiayin Hu, Kai Yuan, Vanessa Hu, Xuetao Yin, Jianhua Li, Sean Suchter

Adaptive Model Compression (AMC): Saliency-Driven Resource Allocation for Ultra-Low-Power Transformer Inference

arXiv:2607. 10109v1 Announce Type: cross Abstract: Deploying large-scale transformer models on resource-constrained edge devices remains a challenge due to the high energy and memory overhead inherent in static inference, which processes simple and complex tokens with uniform intensity.

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arXiv Computer Vision

Sep 3

GaLe: memory-efficient Global Approximate and Local Exact features

GaLe is a memory‑efficient technique that allows pretrained neural networks to run on resource‑constrained devices without retraining. It splits feature maps into a local exact component that keeps fine details and a global approximate component that preserves long‑range dependencies, enabling global operations and attention mechanisms typical of hybrid CNN‑transformer models. On ImageNet, GaLe matches exact‑inference accuracy while delivering up to 65% speedup and 90% RAM reduction on a Cortex‑M33, and it works across classification, detection, and generation tasks.

By Alberto Ancilotto, Elisabetta Farella

llms

arXiv Machine Learning

Sep 25

FlashLoop: Fast and Memory-Efficient Looped Transformers via Lazy Updates

FlashLoop is a training‑free inference framework for Looped Transformers that reduces cross‑loop redundancy by employing token‑sparse updates, sparse attention, and KV‑residual quantization. It exploits observations that, as loops progress, state changes concentrate on a small token subset, attention differences are dominated by a sparse key subset, and KV residuals become amenable to low‑bit quantization. The method achieves lossless accuracy with up to 1.64× speedup and 6× KV‑cache memory reduction across several Looped Transformer models.

By Wanqi Yang, Shiwei Liu

llms efficiency

arXiv AI

Jun 3

PSViT: A Methodology for Structurally Pruning Spiking Vision Transformers

arXiv:2606. 03257v1 Announce Type: cross Abstract: Spiking Vision Transformer (SViT) models are promising low-power ViT models for solving vision-based tasks with state-of-the-art performance.

By Rachmad Vidya Wicaksana Putra, Achyuta Muthuvelan, Alberto Marchisio, Muhammad Shafique

llms computer-vision fine-tuning efficiency benchmarks

arXiv Machine Learning

Aug 17

KV Cache Compression Through the Lens of Transform Coding

arXiv:2608. 14191v1 Announce Type: new Abstract: The key-value (KV) cache stores information from past tokens and is a major memory bottleneck in long-context inference.

By Hannah Laus, Claudio Mayrink Verdun, Hao Wang, Flavio du Pin Calmon, Felix Krahmer

llms efficiency benchmarks

arXiv Computation and Language

Aug 21

Learning how to Forget: Fine-tuning for Long-Context Sparse Attention

arXiv:2608. 19920v1 Announce Type: new Abstract: A lot of prior work addressed key-value (KV) cache selection and compression by sparse attention to enable long-context inference for transformer language models without excessive hardware budgets.

By Matthias Seeger, Zeyu Zhang, Vihang Patil, Konstantinos Benidis, Sebastian Schelter

llms fine-tuning efficiency

arXiv AI

Jun 3

PrimeSVT: An Automated Memory-aware Pruning Framework with Prioritized Compression Policy for Spiking Vision Transformers

arXiv:2606. 03428v1 Announce Type: cross Abstract: The large sizes of Spiking Vision Transformers (SViTs) still hinder their embedded implementation, highlighting the need for model compression.