57 citations · 70 across the 16 of their papers we have counts for
8 papers · 1 filter
MaskCoFT: Masked Co-Adaptive Fine-Tuning for Memory-Efficient MoE Inference
Junfeng Wu, Zehao Fan, Hadjer Benmeziane +3
Mixture-of-experts (MoE) language models often exceed the memory of a single GPU. Expert offloading keeps most experts in host memory and loads them on demand, so decoding speed de…
Context-Aware Mixture-of-Experts Inference on CXL-Enabled GPU-NDP Systems
Zehao Fan, Zhenyu Liu, Yunzhen Liu +4
Mixture-of-Experts (MoE) models scale large language models through conditional computation, but inference becomes memory-bound once expert weights exceed the capacity of GPU memor…
SparseST: Exploiting Data Sparsity in Spatiotemporal Modeling and Prediction
Junfeng Wu, Hadjer Benmeziane, Kaoutar El Maghraoui +2
Spatiotemporal data mining (STDM) has a wide range of applications in various complex physical systems (CPS), i.e., transportation, manufacturing, healthcare, etc. Among all the pr…
AnalogNAS-Bench: A NAS Benchmark for Analog In-Memory Computing
Aniss Bessalah, Hatem Mohamed Abdelmoumen, Karima Benatchba +1
Analog In-memory Computing (AIMC) has emerged as a highly efficient paradigm for accelerating Deep Neural Networks (DNNs), offering significant energy and latency benefits over con…
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…
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…
A Comprehensive Survey on Hardware-Aware Neural Architecture Search
Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi +3
Neural Architecture Search (NAS) methods have been growing in popularity. These techniques have been fundamental to automate and speed up the time consuming and error-prone process…