arXiv:2609.13947v1 Announce Type: cross
Abstract: In-sensor computing reduces the cost of transmitting high-resolution image data by performing early-stage processing near the sensor. However, the lo...
By Chengwei Zhou, Abu Masum, Xuming Chen, Mehran Moghadam, Sreetama Sarkar, Arnab Sanyal, Md Abdullah-Al Kaiser, M. Hassan Najafi, Sercan Aygun, Gourav Datta
The paper introduces a token‑oriented semantic communication framework that transmits only task‑relevant image latents instead of full token embeddings, reducing communication cost and improving interoperability. It leverages a spatial alignment between vision transformer patch tokens and learned image compression latents, enabling token‑level relevance estimation and selective transmission. Experiments on ImageNet demonstrate a superior rate–accuracy trade‑off compared to existing semantic communication methods and hand‑crafted codecs.
By Jiwoong Im, Minwoo Kim, Jaeho Lee, Yo-Seb Jeon, Yongjune Kim
The paper presents a novel multi‑exit computational scheme for TinyML on an ultra‑low‑power GAP9 SoC, adding confidence‑based gating points to a MobileNetV2 CNN for ImageNet‑100. By allowing inference to stop early, the approach cuts average MAC operations by 41 % (from 313 MMAC to 185 MMAC), reduces inference time by 29 % (49 ms to 35 ms), and saves 24 % in energy (2.1 mJ to 1.6 mJ per frame) with only a ~1 % drop in accuracy. Compared to a state‑of‑the‑art adaptive CNN on the same hardware, the method more than doubles computational efficiency, raising MAC/cycle from 8.1 to 17.2.
By Luca Crupi, Lorenzo Lamberti, Alessandro Giusti, Daniele Palossi
The paper proposes a task-oriented semantic feature transmission framework for satellite remote sensing over low‑signal‑to‑noise ratio (SNR) channels. Instead of reconstructing images first, it directly transmits semantic features extracted by a multitask‑pretrained backbone, using a lightweight channel adaptation module to reduce bandwidth and a feature restorer to recover task‑relevant structure after channel corruption. Experiments on scene classification and object detection under additive white Gaussian noise show consistent improvements over reconstruction‑oriented joint source‑channel coding baselines, especially in the low‑SNR regime.
By Shuoyuan Sun, Hongyu Wang, Mugen Peng, Wenjia Xu
arXiv:2609.39296v1 Announce Type: new
Abstract: Video semantic communication has attracted increasing attention as a promising approach to improving video transmission efficiency. However, most exist...
By Xiangben Zhu, Caili Guo, Yang Yang, Chuanhong Liu, Meiyi Zhu
The paper introduces KDG‑SemNOMA, a framework for 6G robotic vehicle networks that combines knowledge distillation and generative models to improve semantic communication over uplink non‑orthogonal multiple access (NOMA). It employs a ConvNeXt‑based deep joint source‑channel coding architecture with an enhanced attention feature module for dynamic channel adaptation, and uses an orthogonal teacher model to guide a NOMA student model via two‑stage knowledge distillation. A channel‑conditional GAN further refines the reconstructed images, yielding higher pixel‑level accuracy and perceptual fidelity on the FFHQ‑256 dataset compared to state‑of‑the‑art methods.
By Qifei Wang, Zhen Gao, Li Qiao, Ziwei Wan, De Mi, Dapeng Li, Ying Sun