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Found 11 papers, 3 papers with code
Lazarus: Resilient and Elastic Training of Mixture-of-Experts Models with Adaptive Expert Placement
The cost of even a single failure is significant, as all GPUs need to wait idle until the failure is resolved, potentially losing considerable training progress as training has to restart from checkpoints.
VcLLM: Video Codecs are Secretly Tensor Codecs
As the parameter size of large language models (LLMs) continues to expand, the need for a large memory footprint and high communication bandwidth have become significant bottlenecks for the training and inference of LLMs.
Adaptive Skeleton Graph Decoding
Large language models (LLMs) have seen significant adoption for natural language tasks, owing their success to massive numbers of model parameters (e. g., 70B+); however, LLM inference incurs significant computation and memory costs.
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.
Curator: Efficient Indexing for Multi-Tenant Vector Databases
Vector databases have emerged as key enablers for bridging intelligent applications with unstructured data, providing generic search and management support for embedding vectors extracted from the raw unstructured data.
AR Visualization System for Ship Detection and Recognition Based on AI
The combination of artificial intelligence and augmented reality technology has also become a future development trend.
Punica: Multi-Tenant LoRA Serving
Our scheduler consolidates multi-tenant LoRA serving workloads in a shared GPU cluster.
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures
With the advent of ubiquitous deployment of smart devices and the Internet of Things, data sources for machine learning inference have increasingly moved to the edge of the network.
How Powerful is Graph Convolution for Recommendation?
In this paper, we endeavor to obtain a better understanding of GCN-based CF methods via the lens of graph signal processing.
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Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation
Inspired by the idea of vector quantization that uses cluster centroids to approximate items, we propose LISA (LInear-time Self Attention), which enjoys both the effectiveness of vanilla self-attention and the efficiency of sparse attention.
Rethinking Lifelong Sequential Recommendation with Incremental Multi-Interest Attention
With the rapid development of these services in the last two decades, users have accumulated a massive amount of behavior data.
