Systems & Networks · Computer Science & AI
Performance
The newest papers in Performance that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
158 papers, from 4 September 2026 to 5 November 2026: 158 arXiv preprints.
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Newest papers, page 8
Accelerating Transfer-Learning-Based Autotuning with Predictive LLVM IR Performance Ranking
As the complexity of High Performance Computing (HPC) ecosys- tems continually increases, achieving optimal performance becomes a challenge.Is INT8 Portable? A Cross-Platform Measurement Study of Quantized Inference on Embedded and Automotive Accelerators
Eight-bit integer (INT8) post-training quantization is the default recipe for edge deployment, under a widely held assumption: INT8 makes inference faster at a small, predictable accuracy cost, and…The AI-Enabled Scientific Frontier
As artificial intelligence's capabilities improve, it is increasingly viewed as a general scientific method.Partition-Aware Scheduling for Mobile Heterogeneous Inference Co-Execution
Modern mobile inference runs on heterogeneous platforms combining mobile GPUs with multiple CPU core clusters.Learning Metastable Dynamics
Metastability---a phenomenon where systems remain trapped in quasi-stable states before abruptly transitioning under rare perturbations---is ubiquitous in physical systems.HELENA for 5G NR LEO NTN Channel Estimation: A Comparative Evaluation
Deep Learning (DL)-based channel estimation has shown high accuracy and low latency in terrestrial 5G NR, but Low Earth Orbit (LEO) Non-Terrestrial Networks (NTNs) introduce Doppler and…HoliBench: A Cross-Platform Benchmarking and Deployment Toolkit for Foundation Models in CPS-IoT Applications
Foundation models, including large language models, vision-language models, and time-series foundation models, are increasingly deployed on embedded and edge platforms for CPS and IoT applications…4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling
We present BEAST, the first-ever Bayesian Swin Transformer for atmospheric forecasting on 0.25$^\circ$ global resolution able to accurately quantify both aleatoric and epistemic uncertainty.Input Resolution Matters: Real-Time Object Detection Latency
We model total latency as the convolution of preprocessing, inference, and postprocessing distributions under a simplifying independence approximation, with selected stage parameters expressed as…Dissecting GPU Utilization for LLM Inference on Nvidia Hopper
A single SM utilization percentage can make an LLM inference workload look compute-saturated while hiding how much useful work is being done.Efficient Vision-Language-Action Management and Serving for Robot Factories
Vision-Language-Action (VLA) models show high robotic manipulation capabilities via a two-stage design: a Vision-Language Model (VLM) stage followed by an Action Diffusion Transformer (ADiT) stage.PELM: Power Efficient On-Device LLM Inference with Speculative Decoding and Dynamic Voltage Frequency Scaling
Deploying Large Language Models (LLMs) directly on mobile platforms at the edge is gaining traction due to a myriad of benefits, such as increased privacy, personalization, and reduced latency.Forward-Free LLM Depth Pruning via Weight Redundancy
Depth pruning reduces large language model (LLM) inference cost by removing complete Transformer blocks.Elastoformer: Enabling Dynamic Adaptivity via Elastic Model Transformation
EdgeAI systems are increasingly employing computer vision applications to enable intelligent, on-device decision-making in real-time.TASTE: Throughput-Aware Batch Size Tuning for On-Device Edge Learning
The rise of privacy-preserving artificial intelligence (AI) has shifted the focus of model adaptation and personalization towards on-device learning, where deep learning models are finetuned…Scalability Analysis of Distributed Kolmogorov-Arnold Network Training on High-Performance Computing Systems
Kolmogorov-Arnold Networks (KANs) replace the fixed activation functions and linear weights of Multi-Layer Perceptrons (MLPs) with learnable univariate functions on network edges, offering improved…RGB Input Pipelines: Throughput, GPU Memory, and Transformation Coverage
An image-augmentation pipeline must deliver a complete batch before a model can use it.RAGMark: A Comprehensive Framework for Benchmarking Retrieval-Augmented Generation Systems
We present RAGMark, a modular benchmarking framework for advanced Retrieval-Augmented Generation (RAG) systems targeting small-scale multi-GPU environments.