5 papers
AESOP: Adversarial Execution-path Selection to Overload Deep Learning Pipelines
Tingxi Li, Mingfang Ji, Ravishka Shemal Rathnasuriya +3
Modern machine learning deployments increasingly compose specialized models into dynamic inference pipelines, where upstream components produce intermediate predictions that determ…
An Empirical Study and Theoretical Explanation on Task-Level Model-Merging Collapse
Yuan Cao, Dezhi Ran, Yuzhe Guo +5
Model merging unifies independently fine-tuned LLMs from the same base, enabling reuse and integration of parallel development efforts without retraining. However, in practice we o…
PARD: Enhancing Goodput for Inference Pipeline via Proactive Request Dropping
Zhixin Zhao, Yitao Hu, Simin Chen +8
Modern deep neural network (DNN) applications integrate multiple DNN models into inference pipelines with stringent latency requirements for customized tasks. To mitigate extensive…
Efficiency Robustness of Dynamic Deep Learning Systems
Ravishka Rathnasuriya, Tingxi Li, Zexin Xu +4
Deep Learning Systems (DLSs) are increasingly deployed in real-time applications, including those in resourceconstrained environments such as mobile and IoT devices. To address eff…
Impact Analysis of Inference Time Attack of Perception Sensors on Autonomous Vehicles
Hanlin Chen, Simin Chen, Wenyu Li +2
As a safety-critical cyber-physical system, cybersecurity and related safety issues for Autonomous Vehicles (AVs) have been important research topics for a while. Among all the mod…