11 citations · 18 across the 8 of their papers we have counts for
8 papers
CodecSight: Leveraging Video Codec Signals for Efficient Streaming VLM Inference
Yulin Zou, Yan Chen, Wenyan Chen +6
Continuous inference over concurrent video streams imposes substantial compute and memory demands on vision-language model (VLM) serving. Streaming inference uses sliding windows t…
Flash-Fusion: Enabling Expressive, Low-Latency Queries on IoT Sensor Streams with LLMs
Kausar Patherya, Ashutosh Dhekne, Francisco Romero
Smart cities and pervasive IoT deployments have generated interest in IoT data analysis across transportation and urban planning. At the same time, Large Language Models offer a ne…
Zelda: Video Analytics using Vision-Language Models
Francisco Romero, Caleb Winston, Johann Hauswald +2
Advances in ML have motivated the design of video analytics systems that allow for structured queries over video datasets. However, existing systems limit query expressivity, requi…
Faa$T: A Transparent Auto-Scaling Cache for Serverless Applications
Francisco Romero, Gohar Irfan Chaudhry, Íñigo Goiri +6
Function-as-a-Service (FaaS) has become an increasingly popular way for users to deploy their applications without the burden of managing the underlying infrastructure. However, ex…
Llama: A Heterogeneous & Serverless Framework for Auto-Tuning Video Analytics Pipelines
Francisco Romero, Mark Zhao, Neeraja J. Yadwadkar +1
The proliferation of camera-enabled devices and large video repositories has led to a diverse set of video analytics applications. These applications rely on video pipelines, repre…
The TRaCaR Ratio: Selecting the Right Storage Technology for Active Dataset-Serving Databases
Francisco Romero, Benjamin Braun, David Cheriton
Main memory database systems aim to provide users with low latency and high throughput access to data. Most data resides in secondary storage, which is limited by the access speed…
INFaaS: A Model-less and Managed Inference Serving System
Francisco Romero, Qian Li, Neeraja J. Yadwadkar +1
Despite existing work in machine learning inference serving, ease-of-use and cost efficiency remain challenges at large scales. Developers must manually search through thousands of…
Mage: Online Interference-Aware Scheduling in Multi-Scale Heterogeneous Systems
Francisco Romero, Christina Delimitrou
Heterogeneity has grown in popularity both at the core and server level as a way to improve both performance and energy efficiency. However, despite these benefits, scheduling appl…