Fast-BEV++ tackles the trade‑off between accuracy and efficiency in vision‑only Bird’s‑Eye‑View perception by adopting two guiding principles: Fast by Algorithm and Deployable by Design. The method decomposes view transformation into a hardware‑oriented Index‑Gather‑Reshape pipeline, removing the need for custom kernels and delivering at least a three‑fold speedup over baseline approaches. On the nuScenes benchmark, Fast‑BEV++ achieves 0.488 NDS while running in real time at over 134 FPS, with depth supervision providing consistent accuracy gains and the architecture enabling seamless deployment on production‑level platforms.
By Yuanpeng Chen, Hui Song, Sheng Yang, Wei Tao, Shanhui Mo, Shuang Zhang, Xiao Hua, Tiankun Zhao
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
arXiv:2607. 00687v1 Announce Type: cross Abstract: Comparing transformer backbones for image segmentation is confounded: each is paired with a different decoder, recipe, and pretraining, so reported differences rarely reflect the backbone itself.
By Tobias Christian Nauen, Anosh Billimoria, Federico Raue, Stanislav Frolov, Brian B. Moser, Andreas Dengel
arXiv:2608. 11770v1 Announce Type: cross Abstract: Edge-deployed vision systems in target recognition, surveillance, autonomous vehicles, and drone domains require hierarchical inference pipelines where a detection model identifies objects of interest and downstream classifiers provide fine-grained attribute analysis.
By Vaishnav Raju
Visual Perception Engine (VPEngine) is a modular framework that enables efficient GPU usage for robotic vision tasks by sharing a foundation model backbone across multiple specialized task heads. It eliminates redundant feature extraction, supports dynamic task prioritization, and achieves up to 3× speedup over sequential execution. The open‑source Python implementation, with ROS2 C++ bindings, delivers real‑time performance (≥50 Hz) on NVIDIA Jetson Orin AGX using TensorRT‑optimized models.
By Jakub {\L}ucki, Jonathan Becktor, Georgios Georgakis, Rob Royce, Shehryar Khattak
arXiv:2607. 22714v1 Announce Type: cross Abstract: Real-time perception is a foundational requirement for advanced driver assistance systems (ADAS) and autonomous vehicles, yet embedded automotive platforms impose severe constraints on compute, memory, and power.
By Sai Sidharth D
The paper introduces RAMP, a method for robust adaptive mixed‑precision quantization of vision models on edge CPUs. It evaluates 13 sensitivity metrics across four neural networks, finding that Jensen‑Shannon Divergence consistently identifies layers that can be safely quantized. Using K‑Means clustering on these metrics, RAMP achieves near‑lossless accuracy with an average 1.81× speed‑up, while cautioning against excluding low‑speed‑up layers that can fragment the computational graph.
By David Poblaci\'on-Criado, Dario Garcia-Gasulla, Eduardo Quinones
TEE-X is a TEE‑aware acceleration framework designed to run large vision models, such as Vision Transformers, entirely within Trusted Execution Environments. It introduces a sensitivity‑aware modularization technique and vectorization to overcome memory constraints and latency challenges on edge devices. The framework is validated on OP‑TEE for Arm TrustZone and optimized for the NVIDIA Jetson AGX Xavier, achieving GPU‑level inference latency with minimal accuracy‑latency trade‑offs.
By Kurt M Wilson, Mohaiminul Al Nahian, Abeer Matar A. Almalky, Sadat Shahriyar, Souvik Kundu, Zhishan Guo, Abdullah Al Arafat, Adnan Siraj Rakin
arXiv:2606. 03748v1 Announce Type: cross Abstract: Real-time vision demands models that are accurate, efficient, and simple to deploy across diverse hardware.
By Glenn Jocher, Jing Qiu, Mengyu Liu, Shuai Lyu, Fatih Cagatay Akyon, Muhammet Esat Kalfaoglu
arXiv:2606. 31938v1 Announce Type: cross Abstract: Deploying Vision Transformer (ViT) models on edge platforms remains challenging due to their high computational demands and the architectural heterogeneity of modern hybrid ViT models, which incorporate both fully connected and convolutional layers.
By Hubert Dymarkowski, Xingjian Fu, Rappy Saha, Jude Haris, Jos\'e Cano
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
Deploying deep learning models on edge CPUs is bottlenecked by computational and memory constraints. Mixed-precision quantization promises to reduce inference latency while preserving accuracy. Howeve...