6 papers
AgentPerfBench: A Benchmarking and Evaluation Suite for Inference Performance of Agentic LLMs
Cheuk Hang Lau, Zeyu Cao, Kevin Wong Cheuk Yin +6
The optimization of LLM serving engines, such as vLLM and SGLang, is largely benchmark-driven: optimizations, scheduling policies, hardware and system designs are all selected base…
MemExplorer: Navigating the Heterogeneous Memory Design Space for Agentic Inference NPUs
Haoran Wu, Zeyu Cao, Yao Lai +16
Emerging agentic large language model (LLM) workloads are driving rapidly growing demand for memory capacity and bandwidth. Different phases of inference, such as prefill and decod…
Supercharging Federated Intelligence Retrieval
Dimitris Stripelis, Patrick Foley, Mohammad Naseri +4
RAG typically assumes centralized access to documents, which breaks down when knowledge is distributed across private data silos. We propose a secure Federated RAG system built usi…
Bringing Federated Learning to Space
Grace Kim, Filip Svoboda, Nicholas Lane
As Low Earth Orbit (LEO) satellite constellations rapidly expand to hundreds and thousands of spacecraft, the need for distributed on-board machine learning becomes critical to add…
Rapid Distributed Fine-tuning of a Segmentation Model Onboard Satellites
Meghan Plumridge, Rasmus Maråk, Chiara Ceccobello +4
Segmentation of Earth observation (EO) satellite data is critical for natural hazard analysis and disaster response. However, processing EO data at ground stations introduces delay…
Space for Improvement: Navigating the Design Space for Federated Learning in Satellite Constellations
Grace Kim, Luca Powell, Filip Svoboda +1
Space has emerged as an exciting new application area for machine learning, with several missions equipping deep learning capabilities on-board spacecraft. Pre-processing satellite…