10 papers
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…
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…
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…
Quantifying the Carbon Reduction of DAG Workloads: A Job Shop Scheduling Perspective
Roozbeh Bostandoost, Adam Lechowicz, Walid A. Hanafy +2
Carbon-aware schedulers aim to reduce the operational carbon footprint of data centers by running flexible workloads during periods of low carbon intensity. Most schedulers treat w…
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…
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…