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Found 13 papers, 3 papers with code
FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving
We present FlashInfer: a customizable and efficient attention engine for LLM serving.
Fine-Tuning Vision-Language Model for Automated Engineering Drawing Information Extraction
These findings highlight the effectiveness of fine-tuning smaller, open-source VLMs like Florence-2, offering a practical and efficient solution for automated GD&T extraction to support downstream manufacturing tasks.
Leveraging Vision-Language Models for Manufacturing Feature Recognition in CAD Designs
To address these challenges, this study investigates vision-language models (VLMs) for automating the recognition of a wide range of manufacturing features in CAD designs without the need for extensive training datasets or predefined rules.
Automatic Feature Recognition and Dimensional Attributes Extraction From CAD Models for Hybrid Additive-Subtractive Manufacturing
Therefore, the proposed methodology enhances the integration of CAD, CAPP, and CAM within hybrid manufacturing by providing precise feature recognition and dimension extraction.
Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing
This paper introduces a cross-modality knowledge transfer (CMKT) methodology for LAM in-situ monitoring, which transfers knowledge from a source modality to a target modality.
In-situ process monitoring and adaptive quality enhancement in laser additive manufacturing: a critical review
Future directions are proposed, with an emphasis on multimodal sensor fusion for multiscale defect prediction and fault diagnosis, ultimately enabling self-adaptation in LAM processes.
Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native
In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures.
Atom: Low-bit Quantization for Efficient and Accurate LLM Serving
To maximize LLMs' serving throughput, we introduce Atom, a low-bit quantization method that achieves high throughput improvements with negligible accuracy loss.
Punica: Multi-Tenant LoRA Serving
Our scheduler consolidates multi-tenant LoRA serving workloads in a shared GPU cluster.
Symphony: Optimized DNN Model Serving using Deferred Batch Scheduling
However, existing model serving systems cannot achieve adequate batch sizes while meeting latency objectives as these systems eagerly dispatch requests to accelerators to minimize the accelerator idle time.
Multimodal sensor fusion for real-time location-dependent defect detection in laser-directed energy deposition
Traditional in-situ monitoring approach utilizes a single sensor (i. e., acoustic, visual, or thermal sensor) to capture the complex process dynamic behaviors, which is insufficient for defect detection with high accuracy and robustness.
Multisensor fusion-based digital twin in additive manufacturing for in-situ quality monitoring and defect correction
Early detection and correction of defects are critical in additive manufacturing (AM) to avoid build failures.
ADARES: Adaptive Resource Management for Virtual Machines
Our large-scale analysis confirms that VMs are often misconfigured, either overprovisioned or underprovisioned, and that this problem is pervasive across a wide range of private clusters.
