SUPREME: A Multi-GPU Framework for Reproducible Image Unlearning Method Evaluation

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.

TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection

arXiv:2606. 06742v1 Announce Type: new Abstract: TorchKM is an open-source library for kernel machines, including support vector machines, kernel logistic regression, and kernel quantile regression, with GPU acceleration.

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.

Mask-Aware Execution for Efficient JEPA Training

arXiv:2609.22674v1 Announce Type: cross Abstract: Joint Embedding Predictive Architectures (JEPAs) are becoming a core representation-learning primitive and a building block for latent world models a...

Accelerate ND-Parallel: A guide to Efficient Multi-GPU Training

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.

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.

InferNet: Exploiting Aggregate GPU Profiles as Side-Channel for DNN Architecture Inference

arXiv:2304. 03388v2 Announce Type: replace Abstract: Deep Neural Networks (DNNs) have become ubiquitous for their ability to solve problems across various domains, including computer vision, natural language processing, and speech recognition.

TEE-X: TEE-aware Acceleration Framework for Large Vision Models at the Edge

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.

Enabling Memory-efficient Im2win Convolution with Multi-precision Support on GPU CUDA and Tensor Cores

arXiv:2608.20725v1 Announce Type: cross Abstract: Convolution is a principal computational bottleneck in deep neural networks, and its efficiency depends on tight integration between algorithms and G...

VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection

arXiv:2607. 06254v1 Announce Type: cross Abstract: Deepfake image detection is currently served by three fundamentally different paradigms: commercial APIs, zero-shot vision-language models (LLMs), and open-source detectors.

HIERA: Workload-Aware Planning Across Implementation Spaces for GPU Kernel Optimization

arXiv:2608.21157v1 Announce Type: cross Abstract: High-performance GPU kernels underpin modern deep learning and scientific computing. As workloads become increasingly diverse and GPU hardware evolve...