arXiv AI
Jun 17
Beyond MACs: Hardware Efficient Architecture Design for Vision Backbones
arXiv:2603. 26551v2 Announce Type: replace-cross Abstract: Vision backbone networks play a central role in modern computer vision.
Fast-BEV++: Fast by Algorithm, Deployable by Design
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.
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FlexViT: A Flexible FPGA-based Accelerator for Edge Vision Transformers
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arXiv Computer Vision
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VGGT-Prime: Compute-Adaptive Mixture-of-Heads for Efficient Visual Geometry Transformers
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arXiv AI
Jul 28
Real-Time Semantic Segmentation with Optimized RetinaNet Architectures for Embedded Automotive Systems
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.
arXiv Computer Vision
Sep 7
Evolving Layer-Specific Scalar Functions for Hardware-Aware Transformer Adaptation
The paper introduces a hardware‑aware framework that uses genetic programming to evolve layer‑specific scalar functions for Vision Transformers, replacing traditional LayerNorm with efficient, heterogeneous approximations. By applying a post‑training re‑alignment strategy, the method eliminates the need for full model retraining while achieving 90‑93% variance capture and recovering over 84% of ImageNet‑1K Top‑1 accuracy for ViT‑B and ViT‑L. The resulting architecture removes the global reduction bottleneck, reducing arithmetic complexity and off‑chip memory traffic, thereby enabling efficient deployment of ViTs on edge accelerators.
arXiv Machine Learning
Aug 13
Achieving Near-Zero-Overhead Multi-Model Hierarchical Classification in Real-Time Detection Pipelines
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.
arXiv AI
Sep 23
LEAP-NBV: Lightweight Edge Active-Perception for Foundation-Model Next-Best-View Planning
arXiv:2609.23974v1 Announce Type: new Abstract: Foundation models are endowing autonomous systems with greater intelligence, enabling a more comprehensive understanding of the environment through vis...
arXiv AI
Sep 17
Visual Perception Engine: Fast and Flexible Multi-Head Inference for Robotic Vision Tasks
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.
arXiv Machine Learning
Aug 14
MergeOver: Post-Training Token Merging for Recursive Vision Transformers
arXiv:2608. 13141v1 Announce Type: cross Abstract: Vision Transformers (ViTs) demonstrate exceptional performance in computer vision but suffer from large parameter counts and quadratic computational complexity, severely limiting their deployment on resource-constrained edge hardware.
arXiv Machine Learning
Jul 2
Soft Mixture-of-Recursions: Going Deeper with Recursive Vision Transformers
arXiv:2607. 00774v1 Announce Type: cross Abstract: Recent recursive Transformer studies have primarily reused shared parameters across computation steps to construct compact, parameter-efficient models.
arXiv AI
Aug 18
Efficient Block-Layer Parallel Inference for Vision-Language-Action on Hybrid Architectures
arXiv:2608. 14586v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are becoming a promising paradigm for autonomous driving, but their deployment on existing vehicle platforms remains difficult because they introduce both high inference latency and strong GPU-side resource pressure.
arXiv Computer Vision
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MapLightning: Online Vectorized HD Map Construction with 1D Map Tokens
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