1 citations · 3 across the 7 of their papers we have counts for
3 papers · 1 filter
MIST: A Co-Design Framework for Heterogeneous, Multi-Stage LLM Inference
Abhimanyu Rajeshkumar Bambhaniya, Hanjiang Wu, Suvinay Subramanian +8
Modern LLM serving now spans multi-stage pipelines including RAG retrieval and KV cache reuse, each with distinct compute, memory, and latency demands. Inference engines expose a l…
FRED: Flexible REduction-Distribution Interconnect and Communication Implementation for Wafer-Scale Distributed Training of DNN Models
Saeed Rashidi, William Won, Sudarshan Srinivasan +2
Distributed Deep Neural Network (DNN) training is a technique to reduce the training overhead by distributing the training tasks into multiple accelerators, according to a parallel…
Demystifying AI Platform Design for Distributed Inference of Next-Generation LLM models
Abhimanyu Bambhaniya, Ritik Raj, Geonhwa Jeong +6
Large language models (LLMs) have shown remarkable performance across a wide range of applications, often outperforming human experts. However, deploying these gigantic models effi…