57 citations · 57 across the 8 of their papers we have counts for
5 papers · 1 filter
Context-Aware Mixture-of-Experts Inference on CXL-Enabled GPU-NDP Systems
Zehao Fan, Zhenyu Liu, Yunzhen Liu +4
Mixture-of-Experts (MoE) models scale large language models through conditional computation, but inference becomes memory-bound once expert weights exceed the capacity of GPU memor…
SparseST: Exploiting Data Sparsity in Spatiotemporal Modeling and Prediction
Junfeng Wu, Hadjer Benmeziane, Kaoutar El Maghraoui +2
Spatiotemporal data mining (STDM) has a wide range of applications in various complex physical systems (CPS), i.e., transportation, manufacturing, healthcare, etc. Among all the pr…
Paged Attention Meets FlexAttention: Unlocking Long-Context Efficiency in Deployed Inference
Thomas Joshi, Herman Saini, Neil Dhillon +2
Large Language Models (LLMs) encounter severe memory inefficiencies during long-context inference due to conventional handling of key-value (KV) caches. In this work, we introduce…
EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media
Ismail Erbas, Ferhat Demirkiran, Karthik Swaminathan +6
Fluorescence LiDAR (FLiDAR), a Light Detection and Ranging (LiDAR) technology employed for distance and depth estimation across medical, automotive, and other fields, encounters si…
Analog Foundation Models
Julian Büchel, Iason Chalas, Giovanni Acampa +7
Analog in-memory computing (AIMC) is a promising compute paradigm to improve speed and power efficiency of neural network inference beyond the limits of conventional von Neumann-ba…