2 citations · 2 across the 8 of their papers we have counts for
9 papers
The Internet of Collaborating Things: Agentic Edge AI for Autonomous Cross-Domain Collaboration
Walid A. Hanafy, Nader Sehatbakhsh, David Irwin +2
The Internet of Things is on a trajectory toward a trillion connected devices deployed across multiple domains. These devices are no longer simple sensing and actuation endpoints;…
FMplex: Model Virtualization for Serving Extensible Foundation Models
Hetvi Shastri, Pragya Sharma, Walid A. Hanafy +3
Foundation models (FMs) are increasingly used as backbones for downstream tasks across language, vision, time-series, and multimodal applications. Yet existing model-serving system…
FM-CAC: Carbon-Aware Control for Battery-Buffered Edge AI via Time-Series Foundation Models
Kang Yang, Walid A. Hanafy, Prashant Shenoy +1
As edge AI deployments scale to billions of devices running always-on, real-time compound AI pipelines, they represent a massive and largely unmanaged source of energy consumption…
Collaborative Processing for Multi-Tenant Inference on Memory-Constrained Edge TPUs
Nathan Ng, Walid A. Hanafy, Prashanthi Kadambi +5
IoT applications increasingly rely on on-device AI accelerators to ensure high performance, especially in low-connectivity and safety-critical scenarios. However, the limited on-ch…
FMTK: A Modular Toolkit for Composable Time Series Foundation Model Pipelines
Hetvi Shastri, Pragya Sharma, Walid A. Hanafy +2
Foundation models (FMs) have opened new avenues for machine learning applications due to their ability to adapt to new and unseen tasks with minimal or no further training. Time-se…
LLM-Driven Auto Configuration for Transient IoT Device Collaboration
Hetvi Shastri, Walid A. Hanafy, Li Wu +3
Today's Internet of Things (IoT) has evolved from simple sensing and actuation devices to those with embedded processing and intelligent services, enabling rich collaborations betw…
MEL: Multi-level Ensemble Learning for Resource-Constrained Environments
Krishna Praneet Gudipaty, Walid A. Hanafy, Kaan Ozkara +4
AI inference at the edge is becoming increasingly common for low-latency services. However, edge environments are power- and resource-constrained, and susceptible to failures. Conv…
FailLite: Failure-Resilient Model Serving for Resource-Constrained Edge Environments
Li Wu, Walid A. Hanafy, Tarek Abdelzaher +3
Model serving systems have become popular for deploying deep learning models for various latency-sensitive inference tasks. While traditional replication-based methods have been us…
CarbonEdge: Leveraging Mesoscale Spatial Carbon-Intensity Variations for Low Carbon Edge Computing
Li Wu, Walid A. Hanafy, Abel Souza +5
The proliferation of latency-critical and compute-intensive edge applications is driving increases in computing demand and carbon emissions at the edge. To better understand carbon…