57 citations · 70 across the 16 of their papers we have counts for
17 papers
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…
On Design Principles for Efficient Heterogeneous DRAM-PIM-GPU Systems
Corey Lammie, Hadjer Benmeziane, William Andrew Simon +1
Heterogeneous DRAM-based processing-in-memory (PIM)-GPU systems promise significant efficiency gains for decode-phase large language model (LLM) inference, particularly in long-out…
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…
Sparse Attention Remapping with Clustering for Efficient LLM Decoding on PIM
Zehao Fan, Garrett Gagnon, Zhenyu Liu +1
Transformer-based models are the foundation of modern machine learning, but their execution, particularly during autoregressive decoding in large language models (LLMs), places sig…
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.…
LionHeart: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles
Corey Lammie, Yuxuan Wang, Flavio Ponzina +7
When arranged in a crossbar configuration, resistive memory devices can be used to execute Matrix-Vector Multiplications (MVMs), the most dominant operation of many Machine Learnin…
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…
Treasure What You Have: Exploiting Similarity in Deep Neural Networks for Efficient Video Processing
Hadjer Benmeziane, Halima Bouzidi, Hamza Ouarnoughi +2
Deep learning has enabled various Internet of Things (IoT) applications. Still, designing models with high accuracy and computational efficiency remains a significant challenge, es…
HyT-NAS: Hybrid Transformers Neural Architecture Search for Edge Devices
Lotfi Abdelkrim Mecharbat, Hadjer Benmeziane, Hamza Ouarnoughi +1
Vision Transformers have enabled recent attention-based Deep Learning (DL) architectures to achieve remarkable results in Computer Vision (CV) tasks. However, due to the extensive…
Skip Connections in Spiking Neural Networks: An Analysis of Their Effect on Network Training
Hadjer Benmeziane, Amine Ziad Ounnoughene, Imane Hamzaoui +1
Spiking neural networks (SNNs) have gained attention as a promising alternative to traditional artificial neural networks (ANNs) due to their potential for energy efficiency and th…
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…