79 citations · 194 across the 39 of their papers we have counts for
6 papers · 1 filter
Clinician-in-the-Loop Smart Home System to Detect Urinary Tract Infection Flare-Ups via Uncertainty-Aware Decision Support
Chibuike E. Ugwu, Roschelle Fritz, Diane J. Cook +1
Urinary tract infection (UTI) flare-ups pose a significant health risk for older adults with chronic conditions. These infections often go unnoticed until they become severe, makin…
HePGA: A Heterogeneous Processing-in-Memory based GNN Training Accelerator
Chukwufumnanya Ogbogu, Gaurav Narang, Biresh Kumar Joardar +3
Processing-In-Memory (PIM) architectures offer a promising approach to accelerate Graph Neural Network (GNN) training and inference. However, various PIM devices such as ReRAM, FeF…
THERMOS: Thermally-Aware Multi-Objective Scheduling of AI Workloads on Heterogeneous Multi-Chiplet PIM Architectures
Alish Kanani, Lukas Pfromm, Harsh Sharma +3
Chiplet-based integration enables large-scale systems that combine diverse technologies, enabling higher yield, lower costs, and scalability, making them well-suited to AI workload…
Designing High-Performance and Thermally Feasible Multi-Chiplet Architectures enabled by Non-bendable Glass Interposer
Harsh Sharma, Janardhan Rao Doppa, Umit Y. Ogras +1
Multi-chiplet architectures enabled by glass interposer offer superior electrical performance, enable higher bus widths due to reduced crosstalk, and have lower capacitance in the…
Direct Prediction Set Minimization via Bilevel Conformal Classifier Training
Yuanjie Shi, Hooman Shahrokhi, Xuesong Jia +3
Conformal prediction (CP) is a promising uncertainty quantification framework which works as a wrapper around a black-box classifier to construct prediction sets (i.e., subset of c…
Atleus: Accelerating Transformers on the Edge Enabled by 3D Heterogeneous Manycore Architectures
Pratyush Dhingra, Janardhan Rao Doppa, Partha Pratim Pande
Transformer architectures have become the standard neural network model for various machine learning applications including natural language processing and computer vision. However…