50 citations · 313 across the 50 of their papers we have counts for
11 papers · 1 filter
ESM: A Framework for Building Effective Surrogate Models for Hardware-Aware Neural Architecture Search
Azaz-Ur-Rehman Nasir, Samroz Ahmad Shoaib, Muhammad Abdullah Hanif +1
Hardware-aware Neural Architecture Search (NAS) is one of the most promising techniques for designing efficient Deep Neural Networks (DNNs) for resource-constrained devices. Surrog…
Democratizing MLLMs in Healthcare: TinyLLaVA-Med for Efficient Healthcare Diagnostics in Resource-Constrained Settings
Aya El Mir, Lukelo Thadei Luoga, Boyuan Chen +2
Deploying Multi-Modal Large Language Models (MLLMs) in healthcare is hindered by their high computational demands and significant memory requirements, which are particularly challe…
TinyCL: An Efficient Hardware Architecture for Continual Learning on Autonomous Systems
Eugenio Ressa, Alberto Marchisio, Maurizio Martina +2
The Continuous Learning (CL) paradigm consists of continuously evolving the parameters of the Deep Neural Network (DNN) model to progressively learn to perform new tasks without re…
SwiftTron: An Efficient Hardware Accelerator for Quantized Transformers
Alberto Marchisio, Davide Dura, Maurizio Capra +3
Transformers' compute-intensive operations pose enormous challenges for their deployment in resource-constrained EdgeAI / tinyML devices. As an established neural network compressi…
RobCaps: Evaluating the Robustness of Capsule Networks against Affine Transformations and Adversarial Attacks
Alberto Marchisio, Antonio De Marco, Alessio Colucci +2
Capsule Networks (CapsNets) are able to hierarchically preserve the pose relationships between multiple objects for image classification tasks. Other than achieving high accuracy,…
Exploring Machine Learning Privacy/Utility trade-off from a hyperparameters Lens
Ayoub Arous, Amira Guesmi, Muhammad Abdullah Hanif +2
Machine Learning (ML) architectures have been applied to several applications that involve sensitive data, where a guarantee of users' data privacy is required. Differentially Priv…
RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks
Alberto Marchisio, Vojtech Mrazek, Andrea Massa +3
Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural Network (DNN) architectures for a given application under given system constraints. DNNs are comput…
Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations
Alberto Marchisio, Beatrice Bussolino, Edoardo Salvati +3
Complex Deep Neural Networks such as Capsule Networks (CapsNets) exhibit high learning capabilities at the cost of compute-intensive operations. To enable their deployment on edge…
PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks
Yue Wang, Wenqing Li, Esha Sarkar +3
Backdoor attacks impose a new threat in Deep Neural Networks (DNNs), where a backdoor is inserted into the neural network by poisoning the training dataset, misclassifying inputs t…
R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors
Alberto Marchisio, Giacomo Pira, Maurizio Martina +2
Spiking Neural Networks (SNNs) aim at providing energy-efficient learning capabilities when implemented on neuromorphic chips with event-based Dynamic Vision Sensors (DVS). This pa…
Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworks
Khadija Shaheen, Muhammad Abdullah Hanif, Osman Hasan +1
Continual learning is essential for all real-world applications, as frozen pre-trained models cannot effectively deal with non-stationary data distributions. The purpose of this st…