4 citations · 4 across the 4 of their papers we have counts for
4 papers
FLINT: Efficiently Leveraging High Bandwidth Flash for Capacity-Scalable LLM Inference Acceleration
Geraldo F. Oliveira, Arash Tavakkol, Xiangyu Zhu +10
LLM inference is increasingly constrained by accelerator memory capacity rather than compute throughput. This constraint is especially acute in single-accelerator and small-node in…
GenPairX: A Hardware-Algorithm Co-Designed Accelerator for Paired-End Read Mapping
Julien Eudine, Chu Li, Zhuo Cheng +11
Genome sequencing has become a central focus in computational biology. A genome study typically begins with sequencing, which produces millions to billions of short DNA fragments k…
Intent-Driven Storage Systems: From Low-Level Tuning to High-Level Understanding
Shai Bergman, Won Wook Song, Lukas Cavigelli +3
Existing storage systems lack visibility into workload intent, limiting their ability to adapt to the semantics of modern, large-scale data-intensive applications. This disconnect…
Leveraging Approximate Caching for Faster Retrieval-Augmented Generation
Shai Bergman, Anne-Marie Kermarrec, Diana Petrescu +4
Retrieval-augmented generation (RAG) improves the reliability of large language model (LLM) answers by integrating external knowledge. However, RAG increases the end-to-end inferen…