2 citations · 4 across the 9 of their papers we have counts for
9 papers
Enhancing Downstream Analysis in Genome Sequencing: Species Classification While Basecalling
Riselda Kodra, Hadjer Benmeziane, Irem Boybat +1
The ability to quickly and accurately identify microbial species in a sample, known as metagenomic profiling, is critical across various fields, from healthcare to environmental sc…
Combining Neural Architecture Search and Automatic Code Optimization: A Survey
Inas Bachiri, Hadjer Benmeziane, Smail Niar +3
Deep Learning models have experienced exponential growth in complexity and resource demands in recent years. Accelerating these models for efficient execution on resource-constrain…
Analog In-Memory Computing with Uncertainty Quantification for Efficient Edge-based Medical Imaging Segmentation
Imane Hamzaoui, Hadjer Benmeziane, Zayneb Cherif +1
This work investigates the role of the emerging Analog In-memory computing (AIMC) paradigm in enabling Medical AI analysis and improving the certainty of these models at the edge.…
FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learning
Sofiane Bouaziz, Hadjer Benmeziane, Youcef Imine +3
Federated Learning (FL) has emerged as a promising Machine Learning paradigm, enabling multiple users to collaboratively train a shared model while preserving their local data. To…
Grassroots Operator Search for Model Edge Adaptation
Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi +1
Hardware-aware Neural Architecture Search (HW-NAS) is increasingly being used to design efficient deep learning architectures. An efficient and flexible search space is crucial to…
AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing
Hadjer Benmeziane, Corey Lammie, Irem Boybat +9
The advancement of Deep Learning (DL) is driven by efficient Deep Neural Network (DNN) design and new hardware accelerators. Current DNN design is primarily tailored for general-pu…