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Found 7 papers, 1 papers with code
HoneyBee: Efficient Role-based Access Control for Vector Databases via Dynamic Partitioning
As vector databases gain traction in enterprise applications, robust access control has become critical to safeguard sensitive data.
HeterMoE: Efficient Training of Mixture-of-Experts Models on Heterogeneous GPUs
However, existing solutions are agnostic to the performance characteristics of different MoE model components (i. e., attention and expert) and do not fully utilize each GPU's compute capability.
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.
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.
