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Found 7 papers, 2 papers with code
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.
