activity
20222024
most citedMany-to-One Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Internet of Things Systems

8 citations · 17 across the 9 of their papers we have counts for

collaborators

9 papers

cs.AR2024

STRELA: STReaming ELAstic CGRA Accelerator for Embedded Systems

Daniel Vazquez, Jose Miranda, Alfonso Rodriguez +3

Reconfigurable computing offers a good balance between flexibility and energy efficiency. When combined with software-programmable devices such as CPUs, it is possible to obtain hi…

cs.AR20242 cited

Performance evaluation of acceleration of convolutional layers on OpenEdgeCGRA

Nicolò Carpentieri, Juan Sapriza, Davide Schiavone +4

Recently, efficiently deploying deep learning solutions on the edge has received increasing attention. New platforms are emerging to support the increasing demand for flexibility a…

cs.LG2024

TimEHR: Image-based Time Series Generation for Electronic Health Records

Hojjat Karami, Mary-Anne Hartley, David Atienza +1

Time series in Electronic Health Records (EHRs) present unique challenges for generative models, such as irregular sampling, missing values, and high dimensionality. In this paper,…

cs.AR20242 cited

X-HEEP: An Open-Source, Configurable and Extendible RISC-V Microcontroller for the Exploration of Ultra-Low-Power Edge Accelerators

Simone Machetti, Pasquale Davide Schiavone, Thomas Christoph Müller +2

The field of edge computing has witnessed remarkable growth owing to the increasing demand for real-time processing of data in applications. However, challenges persist due to limi…

cs.DC20232 cited

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

Darong Huang, Luis Costero, Ali Pahlevan +2

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside…

cs.CR20233 cited

Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties

Jingwei Sun, Zhixu Du, Anna Dai +4

Vertical federated learning (VFL) enables a service provider (i.e., active party) who owns labeled features to collaborate with passive parties who possess auxiliary features to im…